On 2020-10-30 10:11:59, user Hell Maestro Osu! wrote:
Look at the Confidence Interval 0.54 (0.21–1.42), it goes well beyond 1, that's why. A p-value of 0.22 is too high, wether or not you like it.
For the Figure 2 there's obviously not enough patients in the subgroups.
On 2020-04-22 14:15:36, user larrybud wrote:
Counter that argument that the covid death classification is very liberal, thus boosting the death count. (i.e. "anybody who dies with covid is having deemed died FROM covid)
On 2020-04-20 03:50:27, user Tomas Hull wrote:
Those who insist on the selection bias of this study: Would you rather see the ads targeting people working in hospitals and covid19 assessment centres, or those providing essential services to those institutions, like mailmen, delivery men, garbage men, cleaning and maintenance, and so on? <br /> How about people in self-isolation, COVID-19 observation and ICU wards? <br /> Would this kind selection bias satisfy anybody?
On 2020-04-25 21:56:29, user Steve Condie wrote:
You expect herd immunity with undetectable levels of antibodies?
On 2020-04-23 00:41:08, user Fiona Mulvey wrote:
Yes scientists are nitpicking every study on this! The fake data out there is overwhelming and lives are at stake, so of COURSE every study is being ruthlessly ciriticised in every detail - that's what's supposed to happen, with or without a pandemic. That's why peer review typically takes months - this paper would not get published in it's current form. That's why academics have to do a phd defense, to show they're able for the ruthless nitpicking that scientific research requires, and can not only give that criticism, but also take it, with rational responses and without taking it personally. You have to be able to rip up any study and start again, even your own, honestly. It's the only way to keep politics or personal biases out of science, this is not a politcal forum - nothing, NOTHING is sacred. Good scientists will be proud of that - it's a positive for them - we get at truth because we rip apart every claim, systematically, and what survives is a solid theory. Anything else is not science.
On 2020-04-20 08:50:19, user dixon pinfold wrote:
A great many commenters assert that people worried that they'd been exposed to the virus would be over-represented in the study. This seems speculative up to a point, but certainly plausible, and I do not wish to debate it.
Some then go on to assert that anyone else would be too afraid to leave the house. Here I think they are on much less solid ground.
For one thing, not knowing what was in the recruitment ad, we don't know if they were aware that they would remain in their cars, windows rolled up till finger-prick time.
For another, it places a decisively higher estimation on fear (of infection resulting from the single excursion to the testing site) than on curiosity motivated by the desire to be rid of such fear once and for all. This must be true for some people, but for some, surely, it was the other way around.
Then there is the matter of age. Here I freely admit to the speculative element, but it has been for me very easy when out in public in recent weeks to tell by their behaviour how much less fearful younger people are, if they're fearful at all. Who could fail to notice?
On 2020-04-22 08:46:06, user jaxthots wrote:
With self-selected subjects, sampling bias is always a major limitation for generalizing study findings. However, anxiety is probably a common emotional denominator in this sample over the focal existential issues of health and employment that are widely shared by the larger population. More significant bias, perhaps, are the unique characteristics of affluent, high-tech dominated Santa Clara County which, with median household incomes over $100,000 and median SFR values in the $1M range, is unrepresentative of America at large. But with a major airport and lots of foreign professionals visiting Silicon Valley, it has undoubtedly had more exposure to hitch-hiking viruses than most other US populations, which would predict above average infection rates. And similar findings from LA County, Germany and other populations sampled irrespective of varying subject samples and testing methods are providing a clear enough picture to establish a realistic denominator in rate calculations.
Unfortunately, the nominators are far less clear since US hospitals and doctors nationwide have been instructed and incentivized to identify covid-19 as cause of death irrespective of serious co-morbidities, which Italian doctors report are near-universal among the seriously ill and the deaths of presumed covid-19 patients, with definitive test results typically not returned until 2 weeks after the death certificates are signed and recorded. With no possibility before now of valid rate calculations, why are the media inflaming public panic that support draconian protective measures with severe economic consequences by reporting fabricated data?
In addition, many US doctors are reporting peculiar symptoms of "oxygen starvation" without the expected fluid lung congestion of pneumonia, suggesting a different, yet-unidentified disorder. These might possibly involve EMF interacting synergistically with the infection since the pandemic epicenters are in cities that rolled out 5G last year and EMF has been shown to alter voltage-gated calcium channels in hemoglobin molecules that deliver oxygen from the lungs into the circulatory system, as well as other compromising effects on pulmonary functioning.
There are one or more very serious, troubling and suspicious agendas at work here that beg more than perfunctory investigation.
On 2020-04-18 05:53:17, user Grace wrote:
I have heard through the grapevine that positives have been notified.
On 2020-04-18 16:27:11, user Zev Waldman MD wrote:
I agree with other commenters that people who suspected prior Covid infection (or exposure) are more likely to seek antibody testing than those who did not. While participants were asked about prior symptoms, it is not clear what was done with this information. The rate of symptoms/exposure could be reported, compared to the community rates, used as a risk factor for positive antibody testing, etc.
My other concern that has gone less discussed is their calculation of the case fatality rate. While they recognize that reported case numbers as of April 1 are an underestimate, it seems that they forget this skepticism when looking at reported deaths. They seem to take it as a given that 50 people died of Covid in the county as of April 10 as reported, and used this to project to deaths by April 22; however, like case counts, there are multiple reasons to suspect this number of deaths might be higher:
Reporting of deaths is well-known to be delayed - i.e., date of reporting does not equal date of death
People who actually died of Covid may never have been tested, and thus may not be included as cases or deaths
The doubling time of deaths used to project to April 22 is also based on reported deaths; if reporting of deaths is delayed, the doubling time may appear slower than it actually was.
If the death estimate due to illness before April 1 is too low, their corresponding CFR would be an underestimate as well. (This would be exacerbated if their case estimate is too high due to self-selection into the study, as seems possible.) At the very least, some sort of uncertainty around the death estimate should be provider, which in turn would increase the uncertainty around the final CFR.
I know CFR wasn't the main focus on the article, but worry that, because these results support their prior beliefs, some readers may take the results at face value and push them to policymakers before they have been more widely vetted by the scientific community.
On 2020-04-22 05:59:52, user Michael A. Kohn, MD, MPP wrote:
Let's say the truly negative outnumber the truly positive 50 to 1. Then a 0.1% increase in the false positive proportion gets you the same number of errors as a 5% increase in the false negative proportion. That's why the false positive proportion is more important.
If specificity is perfect (there are no false positives), then a lower sensitivity (higher false negative proportion) would lead to an underestimate of seroprevalence. You should multiply the the proportion with a positive test by 1/sensitivity to get the seroprevalence.
On 2020-10-17 03:11:04, user AB wrote:
What constituted the standard of care arm in different countries and different centres? Did every centre use steroids? Or some did and some didn't for severe cases? In absence of specifying SOC, are we sure we are using a standard comparator for the intervention (experimental drug arms)?
On 2020-12-24 07:31:39, user K Cornwell wrote:
Well done on your study. It is because of doctors who go the extra mile in the fight against this terrible virus. That we find that some of our medicines which may have been around for many years are having a significant impact on the treatment and recovery times. Let hope that the vaccines are enough to create some immunity across the countries and the treatment algorithms improve with better research.
On 2022-01-08 19:16:50, user madza wrote:
Excuse my ignorance but when SAR % is lower in unvaxxed than in vaxxed, how come the Conclusion is that vax reduces the transmissibility?
On 2021-12-14 04:16:54, user Gregory C. Belmont wrote:
Urgently, the title of this report should be modified to reflect reduction of neutralisation vs previous variants, not reduction vs unvaccinated serum. The lay public is misinterpreting the title and reported results to mean vaccination reduces individual immune response in the vaccinated population vs unvaccinated population. See the following Drudge Report headline and tweet as two of many examples of such misinterpretations in addition to articles published by mainstream media journalists who do not know how to read scientific papers. In addition to a more comprehensively crafted title, the report test should explicitly mention the absence of unvaccinated control serum.
https://ibb.co/yRr4Xxq<br /> https://ibb.co/s3Dgk4B<br /> https://twitter.com/ChuckLo...
On 2020-07-02 17:21:31, user Felice Bruno wrote:
what is the dosing protocol for Ivermectin used in the study ?
On 2025-06-21 07:53:30, user Laszlo Szabo wrote:
Fig 3 / c<br /> Plot labels and fig caption are different. Check which colour represents the new and original reviews.
On 2020-06-16 02:40:00, user Vijai Ram wrote:
On 2021-09-07 15:50:49, user Zach wrote:
Im confused if it doesn't reduce death significantly statistically and increases serious adverse events by double. Why is this being pushed as effective or safe? This data proves both to be wrong. If your chance of death is unchanged and your hospitalization rate is nearly doubled it literally makes no sense to take this. What am I missing?
On 2021-08-12 16:12:41, user pedro paulo castro wrote:
It's VAERS
On 2021-07-31 16:02:08, user Mark wrote:
On 2020-05-08 08:24:06, user David Curtis wrote:
Thanks for these comments. Sorry, I've only just seen them. MedrXiv doesn't seem to send notifications.
Let's see.
1) We don't know anything about exposure. It's not a case-control study of those exposed who do or don't get infected, or who do or don't have a severe reaction. There is an assumption that only a small minority of infected subjects require hospitalisation, maybe 5-10%, so that the people tested represent a susceptible minority. Then the very large number of “controls” are dominated by the 90% or more would not have a severe response, even if exposed. One can regard the controls as providing background allele frequencies.
2) If one had a large sample with antibody results that would be very helpful in pulling apart who had been infected but had a mild response and who a severe response. I’m hoping that this will be seen to be a sensible thing to do with UK Biobank subjects and that when good antibody tests are widely available this might be arranged. It would be good if they especially focussed on the exome-sequenced subjects.
3) I think the variant calling was OK. Most variants passed QC. I don’t see why it should be worse for these genes than any others. I feel fairly confident that it’s unlikely that there’s an important variant there which has been missed.
4) Yes, it would be perfectly reasonable to have age as a covariate. But bear in mind that even in the elderly, only a minority are severely affected. So the controls are still giving a good idea of background allele frequencies. My main concern was about ethnicity. There was a risk that including population principal components as covariates would mask ancestry effects. That’s why it’s good that both analyses are negative – with and without principal components.
5) I was careful to state what hypotheses are not true. There may be very rare variants with high odds ratios. There are no common or fairly common variants with large effect sizes. I didn’t want to put too much speculation in the paper but I think this means that it is unlikely that genetic effects contribute much to differences in severity related to ethnicity. If there had been one or two variants which had a big effect in this (mostly Caucasian) sample then it’s plausible that they might have different frequencies in different ethnicities. However, if only very rare variants exert substantial effect then one would have to speculate that in some ethnicities there was overall a large excess of many different rare variants and this does not seem a likely scenario. I don’t think my results are particularly susceptible to the kind of misinterpretation you suggest. It’s widely understood that these proteins are important. My results just show that naturally occurring variation in these genes is not a big driver of variation in susceptibility to severe illness.
I guess the only thing I’d add is that I deliberately included a list of all variants so that people could look down them and get a general sense of how much variation there is, how similar are allele frequencies between cases and controls, etc. When one looks at the raw data one gets a pretty strong sense that it isn’t these variants which are determining who does and doesn’t get severe illness. And this exactly confirms my prior expectations. We have lots of experience with infectious illness and important genetic effects are few and far between. Likely a lot of the difference is driven by fairly random factors in terms of the individual immune response. However the difference in severity between ethnicities is an extremely important topic, especially in the UK, so I think it’s useful to communicate that there is not an obvious genetic explanation for this.
Anyhow, I hope this helps and thanks for your interest.
On 2025-08-28 11:53:18, user Richard Bradbury wrote:
The published version of this text is now available at:<br /> https://wwwnc.cdc.gov/eid/article/31/9/24-1923_article
On 2020-05-07 02:09:58, user Mauro Morais wrote:
These calculations rely on wrong assumptions of the modeling.
On 2020-11-13 15:19:50, user Abhay Sharma wrote:
This finding that moderate adult COVID-19 patients administered a single dose of intradermal BCG achieve faster resolution of hypoxia, and significant radiological improvement and viral load reduction, without showing evidence of BCG induced cytokine storm, is supported by a recent epidemiological transcriptomic evidence that BCG vaccination induces persistent upregulation of antiviral defense response and downregulation of myeloid cell activation in blood cells (https://doi.org/10.1101/202... "https://doi.org/10.1101/2020.11.10.374777)").
On 2021-06-14 04:23:29, user Eyad Qunaibi wrote:
This article has been peer-reviewed and published: <br /> https://elifesciences.org/a...
On 2021-03-18 20:59:19, user Dr Ahmed Tareq wrote:
Dear "External Reviewer"
When we discuss academic research, it is much better to discuss the merits and the disadvantages of the study rather than putting claims regarding the author persona.
Your approach to question the paper quality by putting false claims about the author is itself an anti-scientific approach.
Needles to say, all the information contained in the paper are well-cited and were retrieved from trusted organizational sources such as the WHO, the CDC, and from other published academic research papers. Your comments should have discussed the methodology and proposed constructive arguments, rather than discussing the author. You did not highlight where is the claimed "bias" in the results. Additionally, you did not explain any statistical issues in the results that could make them biased or unverifiable.
The results presented in the paper coincide with the results presented in other papers cited within. The results are consistent with the "public" view in many countries about vaccination resistance. The researchers here present these results so that the academic institutions and governments address these issues by raising the awareness of the public, rather than ignoring the data. Choosing to live in denial is the anti-scientific approach, not the other way around.
Lastly, the fact that there is a huge sample contributing to the study is a positive point and should not be a critical point. The large number of participants is a plus here since it helps to avoid the issues usually associated with small samples (for example, non-representation, statistical insignificance, etc...).
Next time when you discuss a research paper, please be constructive and add something valuable about the design, the methodology, or the approach, rather than offering your personal views about the author.
Regards,<br /> Ahmed
On 2021-03-19 09:10:29, user Eyad Qunaibi wrote:
We disagree with these unsubstantiated and diffuse claims. The high rates of vaccine hesitancy were reported in tens of studies preceding and contemporary to this study, as clarified in the Discussion section. For example, Lin et al. (citation at the end of the comment) conducted a systematic review that included 126 studies and surveys and showed a decline in vaccine acceptance. In addition, IPSOS (citation below) published the results of a survey with 18,526 participants from 15 countries widely distributed geographically with the title “COVID-19 vaccination intent is decreasing globally”. The high rates of vaccine hesitancy in Arab region have been reported in other studies as detailed in the Discussion section. In fact, reasons related to conspiracy were higher in these other studies; for example, in the study by Sallam et al. (ref 10 in the paper), 40.3% of Arabs believed that COVID-19 was manufactured to enforce vaccination, in comparison with 18.4% in this study who chose (Coronavirus is a conspiracy and the vaccine is part of this conspiracy). The most frequent concerns about the vaccine reported in this study are very similar to those reported world-wide. <br /> The use of social media to recruit participants has been applied in several other studies on vaccine hesitancy. See references 4-7 below as examples, in addition to several other examples in the study of Lin et al. <br /> The research was conducted in a scientific way and the paper was written in a scientific language and based on scientific evidence from scientific publications and well-known institutes, to raise the awareness about the problem of vaccine hesitancy and help in addressing it, which is just against "science denying" or “antivaccination”! All the authors are prominent researchers with collectively hundreds of publications in high reputation journals and thousands of citations.<br /> Here are the citations for the information in our comment: <br /> 1. Lin, C., et al. Confidence and Receptivity for COVID-19 Vaccines: A Rapid Systematic Review. Vaccines 9, 16 (2020).<br /> 2. COVID-19 vaccination intent is decreasing globally | Ipsos (2020).<br /> 3. Sallam, et al. High Rates of COVID-19 Vaccine Hesitancy and Its Association with Conspiracy Beliefs: A Study in Jordan and Kuwait among Other Arab Countries. Vaccines 9, 42 (2021).<br /> 4. Rahul S, et al. COVID-19 Vaccine Acceptance among Health Care Workers in the United States. Vaccines (Basel) 3;9(2):119 (2021).<br /> 5. Gagneux-Brunon A, et al. Intention to get vaccinations against COVID-19 in French healthcare workers during the first pandemic wave: a cross-sectional survey. J Hosp Infect. 108: 168–173 (2021).<br /> 6. Qattan A, et al. Acceptability of a COVID-19 Vaccine Among Healthcare Workers in the Kingdom of Saudi Arabia. Front. Med. 8: 1-12 (2021). <br /> 7. Al-Mohaithef M and Padhi B. Determinants of COVID-19 Vaccine Acceptance in Saudi Arabia: A Web-Based National Survey. Journal of Multidisciplinary Healthcare 13: 1657-1663 (2020).
On 2021-02-04 18:11:23, user Gina Assaf wrote:
Do the researchers have data on the percentage of those with autoantibodies who also developed IgM and IgG COVID antibodies from this cohort? My question is getting at if autoantibodies are mutually exclusive with antibodies. The reason I ask is we have a large cohort of long COVID patients who reported tested negative for antibodies in our patient-led research survey (most of them were tested post 3 months from infection though). https://www.medrxiv.org/con...
On 2021-06-26 12:51:39, user Grzegorz A Rempala wrote:
A paper from our group on modeling repeated testing on OSU campus. We hope that the methodology can be used in other situations when vaccination is not an option and repeated testing is requited. Thanks to all who contributed to data collection for this project !
On 2021-10-31 17:00:24, user wincap wrote:
You should include affect of vaccines triggering paraneoplastic syndrome, which occurs in 20% of cancer patients.
On 2020-12-11 10:13:56, user Marina Pollán wrote:
Please, notice that a new version of this paper, including additional information, has been accepted and published in the British Medical Journal:<br /> doi: 10.1136/bmj.m4509<br /> Prof. Marina Pollán in the name of all the authors
On 2020-03-31 22:30:20, user Aaron wrote:
While the author claims that "The data suggest that at least two strains of the 2020 SARS-CoV-2 virus have evolved during its migration from Mainland China to Europe", no such data is presented or referenced. The title of the piece should be changed to reflect that this is a hypothesis, not a finding established by any analysis found within the paper.
On 2020-04-26 13:24:24, user Rosemary TATE wrote:
Hi, <br /> I'm struggling to understand the results. Eyeballing the graphs in figure 2, the best fit appears to be with Nitrogen dioxide, yet the R (do you mean rho?) is the lowest. Ditto the R2. I assume you use linear regression to obtain the fitted lines, although no mention is made of this in the methods.
Also, I would expect levels of pollution to be higher in regions with highest populations, which would similarly be expected to have more deaths. Did you think of controlling for this, or alternatively using the death rate rather than reported cases?
It would be very helpful if you included a table with the number of deaths and pollution levels for each region - or at the very least label the graphs.
What was the rationale for collapsing the 120 sites into only 7 regions - it would be much more useful if this was more fine grained.
And yes, please can you send me your data<br /> Thanks
On 2021-11-09 17:19:35, user MrMinerUndercover wrote:
How exactly are you people criticizing CDC studies for their construction; when this study doesn't even tell you the number or nature of the test subjects.<br /> It just posits something without any actual data.<br /> Finally, the people whom the consider to have natural immunity caused from getting covid INCLUDES people who got covid, and 1 shot. Would this not stray the data?
On 2021-09-14 20:36:13, user Dedo v. Krosigk wrote:
Isn't there an very important variable missing? During the follow-up period of June 1 to August 14, 2021 the incidence rose from nearly zero to over 400. But the incidence is not part of the described covariates. So the results of the study are only meaningful if the mean incidence (corresponding to the risk of infection) at the date of infection was comparable between the groups of previously infected and vaccinated.
On 2021-09-03 11:23:16, user Toomas Fox wrote:
This study didn't include people who "already had and survived it" and subsequently received FULL vaccination. The study is fundamentally flawed because they chose to exclude an extremely important variable from their equation.
On 2021-09-04 14:32:44, user 4qmmt wrote:
There are a number of studies looking at the question of the risk of the vaccine versus the risk of Covid. See the recent Clalit study from Israel that claims to do just that. DOI: 10.1056/NEJMoa2110475
But read carefully, as they omit large populations which would have clearly impacted their results and the follow-up time period is very short, only 21 days after 2nd dose, meaning that they are not counting any problem that takes longer than 21 days to develop and get reported to Clalit's system. That is important, because any treatment outside of hte Clalit system, such as emergency hospitalization, may not get reported into the Clalit system until weeks or months later.
Another main problem is that they try to present an either/or scenario wherein the risks associated with vaccination or infection are supposedly compared. However, as we all know, vaccinated individuals are getting infected also, meaning that they carry the risk of both situations. I can also tell you right now, according the Israeli Ministry of Health, the great majority of deaths in Israeli hospitals are among vaccinated patients.
On 2021-09-03 13:05:33, user Jeremy R. Hammond wrote:
Indeed, I was not misinterpreting the study's findings. They did not demonstrate a significant benefit of people who've acquired natural immunity getting a single dose of vaccine. Instead, their result only attained statistical significance when they included people who received a single dose of vaccine and then were vaccinated.
The media are falsely reporting this study as showing a significant benefit of vaccinating people with natural immunity. Science magazine, for example, reported this (bold emphasis added):
"The researchers also found that people who had SARS-CoV-2 previously and then received one dose of the Pfizer-BioNTech messenger RNA (mRNA) vaccine were more highly protected against reinfection than those who once had the virus and were still unvaccinated."
That original version of the article is archived here:
https://web.archive.org/web...
I confronted the author and the magazine on Twitter, and the author acknowledged that their claim was incorrect. She said they had "corrected" it, but all they did was remove the "then" that I've bolded, so that anyone reading it would still assume that it meant that the study found a significant benefit of vaccinating people with natural immunity.
I replied:
"No, you did not correct it. You just deleted "then" so that instead of your statement being outright false, it is now just highly misleading. You need to tell the truth: people who had infection THEN got vaccinated were NOT shown to have received a benefit from the shot."
But she insisted that it was now "accurate" and refused to clarify for Science readers that they did not find a statistically significant benefit of vaccinating people with natural immunity. That Twitter thread is here:
https://twitter.com/jeremyr...
News Medical, too, is falsely characterizing the findings of this study, saying: "Results showed that a single vaccine dose with natural immunity provided greater protection against reinfection than people with natural immunity alone."
https://www.news-medical.ne...
I have also called on the author of that piece and the publication to correct their false claim, but as of now, they have not responded.
In fact, the study authors explicitly state in the body of their paper that they found no statistically significant benefit when individuals with natural immunity received a single dose of vaccine. The result only achieved statistical significance when including individuals whose immune systems were primed by vaccination and who then were infected.
Thus, rather than suggesting a benefit of vaccination for naturally immune people, the findings suggest a benefit of infection for vaccinated people.
It is little wonder that the media are falsely characterizing the findings of the study given that the authors themselves mischaracterize their own findings in the abstract, stating:
"Individuals who were both previously infected with SARS-CoV-2 and given a single dose of the vaccine gained additional protection against the Delta variant."
The only logical interpretation of that statement is that they found a statistically significant benefit of vaccination for people with natural immunity, but they might just as well have summarized, "Individuals who were given a single dose of the vaccine and infected with SARS-CoV-2 gained additional protection against the Delta variant."
The responsible thing for the authors to do would be to publicly clarify their findings and to correct their wording so that the abstract accurately summarizes their actual findings so that the media don't continue to falsely claim that the study found a significant benefit of vaccination for people with natural immunity.
On 2022-01-15 12:05:39, user Anzola wrote:
Reading for comprehension? Why is everyone missing the critical text at the top that says this is a preprint and has not been peer-reviewed?
MedRxiv:<br /> “We also urge journalists and other individuals who report on medical research to the general public to consider this when discussing work that appears on medRxiv preprints and emphasize it has yet to be evaluated by the medical community and the information presented may be erroneous.”
On 2021-10-31 12:17:40, user Heath wrote:
The study had a bizarre design.
The agency’s researchers looked at 200,000 people who had been hospitalized<br /> with “Covid-like” illnesses from January through August in nine states.<br /> Right away, this choice sets up the study in a problematic way; for <br /> most of that time, people who had received Covid vaccines believed <br /> (because the CDC and others told them) that they were at VERY low risk <br /> of getting Covid, and certainly symptomatic Covid. Thus they may have <br /> been less likely to go to the hospital at all, or be tested for Covid <br /> once they arrived.
But put that aside.
Then the researchers decided to compare two groups - people who had <br /> definitely had Covid at least 90 days before and received another Covid <br /> test around the time of their hospitalization and people who had been <br /> fully vaccinated at least 90 days (but no more than 180 days) before and<br /> received a Covid test around the time of their hospitalization.
This choice is also bizarre. Those of you who have been paying attention <br /> will know that this date range is designed to make the vaccines look as <br /> good as possible by testing in the happy vaccine valley, the short <br /> period when mRNA vaccines are at maximum effectiveness (in fact, they <br /> are probably starting to lose it by the sixth month).
But more importantly, this criteria excluded the VAST majority of the <br /> people hospitalized with Covid-like illnesses or tested for Covid.
Only about 1,000 people out of the 200,000 people hospitalized for <br /> Covid-like illnesses over the eight months had a previous documented <br /> Covid infection. (Given the fact that at least 20 percent of Americans, <br /> and probably more like 40 percent, had had Covid by the spring of 2021, <br /> this is a strikingly small percentage - and certainly doesn’t suggest <br /> long Covid is much of a threat.)
And only 89 of those 1,020 people with natural immunity tested positive. In<br /> contrast, 324 out of the 6,328 vaccinated people who met the study’s <br /> criteria tested positive.
But isn’t 324 more than 89?
It sure is. And the CDC didn’t have - or didn’t publish - figures on how <br /> many people were actually in the two groups - those with natural <br /> immunity and those infected. Instead it compared the PERCENTAGE OF <br /> POSITIVE TESTS in the two groups.
But why would the percentage of positive tests matter, when we don’t know how many people were actually at risk?
Great question.
But, amazingly, the statistical manipulation then got even worse.
The natural immunity group had an 8.7 percent positive test rate. The fully<br /> vaccinated group had a 5.1 percent positive test rate. So the natural <br /> immunity group was about 1.7 times as likely to test positive. (1.7 * <br /> 5.1 = about 8.7.)
With such a small number of people in the natural immunity group, that raw <br /> “rate ratio” may well have failed to reach statistical significance. (We<br /> don’t know, because the CDC didn’t provide an unadjusted odds ratio <br /> with 95% boundaries - something I have never seen before in any paper.)
Instead, the CDC provided only a risk ratio that it had adjusted with a variety <br /> of factors, including “facility characteristics [and] sociodemographic <br /> characteristics.”
And finally, the CDC’s researchers got a number that they could publish - <br /> hospitalized people who had previously been infected were five times as <br /> likely to have a positive Covid test as people who were fully <br /> vaccinated. Never mind that there were actually four times as many <br /> people in the second group.
Science!
By the way, buried at the bottom of report is some actual data. And it’s bad.
The CDC divided the hospitalizations into pre- and post-Delta - January through June and June through August.
On 2021-10-20 16:49:53, user Emily Wood wrote:
This paper has been accepted for publication in SAGE Open Medicine.
Please cite as:<br /> Wood, E., Bhalloo, I., McCaig, B., Feraru, C., & Molnar, M. (2021). Towards development of guidelines for virtual administration of paediatric standardized language and literacy assessments: Considerations for clinicians and researchers. SAGE Open Medicine. https://doi.org/10.1177/205...
On 2021-12-15 14:21:44, user Ole K. Fostad wrote:
Defining unvaccinated up to 21 days after the first vaccine dose is problematic. This definition will mask the possibility of an increase in infections due to the vaccine in the 21 first days after vaccination. The paper does neither discuss nor justify this definition and the corresponding possibility of survivorship bias in the result due to this.
On 2024-11-08 16:21:12, user Kristin Ressel wrote:
Changes to this manuscript were made during the article submission process to the journal Archives of Physical Medicine & Rehabilitation. It is now published and can be found using the citation provided below.
Freburger, J. K., Mormer, E. R., Ressel, K., Zhang, S., Johnson, A. M., Pastva, A. M., Turner, R. L., Coyle, P. C., Bushnell, C. D., Duncan, P. W., & Berkeley, S. B. J. Disparities in Access to, Use of, and Quality of Rehabilitation Following Stroke in the United States: A Scoping Review. Archives of Physical Medicine and Rehabilitation. https://doi.org/10.1016/j.apmr.2024.10.010
On 2021-05-29 15:27:58, user Thomas Eddlem wrote:
I'm skeptical of both studies. The one you linked to has a small sample size, and I don't like that it reported a coefficient (140 cases/100,000), which is small, and doesn't note that there were wide overlapping confidence intervals in its conclusions. Really wide. Did you check the chart?
This study on this page seems anti-mask agenda-driven, and its conclusions in the summary are over-generalized. There probably is not a statistically significant difference between mask-mandate states and non-mask mandate states, but that doesn't mean masks never work.
A mandate to wear a mask walking down a city street is not the same as a mandate to wear one when visiting someone in a hospital or a brief visit in some other close-up situation. But both studies tend to treat these as roughly the same.
There's clearly a lot more research needed on this. Most of the studies done in 2020 are junk, proven inaccurate by the historical data.
On 2021-05-28 23:54:05, user beamansurchit wrote:
A legitimate question. Will you take it at face value after it has been peer reviewed?<br /> I've been looking into the science behind mask mandates for a while now and there is literally NOT ONE RCT study which has found masks (or respirators for that matter) to be effective to reduce the transmission of viral illness. Other things like droplets, sure, but not viral illness.
On 2021-05-27 15:18:44, user Jäger wrote:
And what you're continuing is cherrypicking fear mongering - not science. With word salad cherrypicking, hoping to get away with calling it "science".
Jan 2021 U.S. Department of Health and Human Services
According to the current knowledge, the virus SARS-CoV-2 has a<br /> diameter of 60 nm to 140 nm [nanometers (billionth of a meter)],<br /> while medical and non-medical facemasks’ thread diameter ranges<br /> from 55 µm to 440 µm [micrometers (one millionth of a meter),<br /> which is more than 1000 times larger. Due to the difference in<br /> sizes between SARS-CoV-2 diameter and facemasks thread diameter,<br /> SARS-CoV-2 can easily pass through any facemask.
A meta-analysis among health care workers<br /> found that compared to no masks, surgical mask and N95<br /> respirators were not effective against transmission of viral<br /> infections or influenza-like illness based on six RCTs. Using<br /> separate analysis of 23 observational studies, this meta-analysis<br /> found no protective effect of medical mask or N95 respirators<br /> against SARS virus. A recent systematic review of 39 studies<br /> including 33,867 participants in community settings (self-report<br /> illness), found no difference between N95 respirators versus<br /> surgical masks and surgical mask versus no masks in the risk for<br /> developing influenza or influenza-like illness, suggesting their<br /> ineffectiveness of blocking viral transmissions in community<br /> settings.
On 2020-04-13 09:54:09, user FabioMassimoFabrizio wrote:
The study of France was controverified by france INSERM: "No Evidence of Rapid Antiviral Clearance or Clinical Benefit with theCombination of Hydroxychloroquine and Azithromycin in Patients withSevere COVID-19 Infection": https://www.sciencedirect.c...
On 2020-08-17 22:15:16, user Roy Verdery wrote:
Neutralizing activity assessed using Vero cells may not reflect clinical<br /> activity. Nature (2020). https://doi.org/10.1038/s41....
On 2020-11-27 19:55:21, user John Butler wrote:
There seems to be either something wrong with the risk calculator or the paper text. If I choose White Female age 70-74, no comorbidities, it says 18.9% higher. I take it that means "multiply the base rate time 1.189". If I switch that to "male" it reports 119.1% higher, which would, to be consistent with the female, mean "multiply the base rate times 2.191". However, if I select Hispanic Male, 80-84 with Chronic Kidney Disease, it report 601.8%, the text reports "6 times higher". All this suggests that the White Female 70-74, as an example, is inconsistent with the form of the others.
On 2025-07-14 22:06:03, user Pedro Rocha wrote:
This work has been published in its entirety in a peer-reviewed journal. The published version is available at: https://journal.unoeste.br/index.php/cv/article/view/5016/3709 , doi: 10.5747/cv.2024.v16.v379
On 2020-07-01 19:54:15, user Joao Borges wrote:
Sorry but your are wrong. Flu vaccination in Brazil is paid by the government for those who could be at risk (60+ years old, healthcare workers, exposed workers, pregnant women etc) .
Actually most vaccination in Brazil is carried by the government. Even middle class ppl who pay for private healthcare use the same system for vaccines, as is kinda a cultural thing in Brazil.
Everybody is used to be vaccinated against a lot of diseases here, since childhood.
So I fail to see this number bias that you are pointing out
On 2025-11-11 03:32:18, user Evolutionary Health Group wrote:
We at the Evolutionary Health Group ( https://evoheal.github.io/) "https://evoheal.github.io/)") really enjoyed this paper.
Here are our highlights:
In the week after the Jan 7 ignitions, virtual (clinic) respiratory visits jumped 41% in highly exposed areas and 34% in moderately exposed areas, totaling 3,221 excess visits, a clear, short-term signal health systems can act on.
Virtual cardiovascular visits rose by ~35% across exposure groups in that first week (~2,424 excess visits), pointing directly to surge planning for virtual care during wildfire weeks.
On the day of ignition (Jan 7) in highly exposed areas, outpatient neuropsychiatric and injury visits were about 18% higher than expected, evidence that mental-health demand starts immediately, not just respiratory care.<br /> The exposure framing is reproducible: simple proximity bands (<20 km vs >=20 km within LA County) applied to a 3.7-million-member health system and a five-category visit dashboard (all-cause, cardiovascular, injury, neuropsychiatric, respiratory) that others can copy.
Scaled to all LA County residents, the estimates imply ~16,171 excess cardiovascular and ~21,541 excess respiratory virtual visits in the week after ignition, strong justification to expand virtual capacity during major fires.
On 2020-04-20 08:02:18, user Andrew the longwinded wrote:
Good point. That would put NYC death rate at 0.16% and climbing, well above flu death rates.
Don't know where you got the 1600/million from, but since worldometer ( https://www.worldometers.in... ) puts NY state at 933/million (0.0933%) I'm inclined to believe that figure.
On 2020-05-07 21:12:07, user helgarhein wrote:
Would you please link the outcomes of covid illness (mild, severe, death) to 25(OH)D? I imagine lowest vitamin D levels linked to worst outcomes, like in other studies. Thanks
On 2021-05-28 13:37:01, user Jose Usme wrote:
Here you can access the last published version: https://wwwnc.cdc.gov/eid/a...<br /> DOI: 10.3201/eid2612.202969
On 2021-03-30 12:09:03, user jgas wrote:
Has there been furtheer follow-up beyond 72 hrs?<br /> This data would really help to clarify possible mechanism of serious adverse effects emerging with the roll-out of the Oxford adenovirus vector vaccine to frontline workers across Europe and the safety of use of these adenovirus vectors per se.
On 2021-08-31 05:02:22, user kdrl nakle wrote:
Since contact tracing depends heavily on the level of community transmissions there is no way it can be used successfully in the situation where that level is high. The most effective measure in that situation is vaccination regardless of mathematical models.
On 2020-12-14 15:13:45, user GH wrote:
The authors note that "The initial decrease in suicide rates during 1913-1918, with the lowest rate of 9.97 per 100 000 in 1918, was followed by an increase with the highest peak of 22.15 per 100 000 reached in 1970". Interestingly 1918 is the year before the spanish flu pandemic, and even more interestingly one of the mentioned peaks occurs in 1922 (15/100 000) (at the end of the pandemic). This is a 50% increase in 4 years, probably the biggest increase in the entire dataset. Doesn't this suggest a post/during-pandemic-effect at least for the spanish flu?
On 2020-04-17 07:35:29, user Mansour Tobaiqy wrote:
Thank you, the paper has bow been published at IPIP:
On 2022-01-09 23:14:33, user Neil Bogdanoff wrote:
So why no mention about how people can test positive with both antigen and PCR tests for Covid-19 for up to three months after infection? And how might that portion of the population impact the results of the study? Also, is there some analysis as to why the different type of vaccines might play a role? And of the population studied, which vaccines did they receive--and what percentage had received booster shots? When will the study be peer-reviewed?
On 2023-02-20 07:41:20, user Martin Schecklmann wrote:
I miss in the discussion the meaning of relevant dose of TMS with respect to e-field. We could demonstrate that F3-based method is similar to neuronavigation-based method with respect to clinical efficacy even if induced e-field was lower for F3-method (doi: 10.1016/j.brs.2021.01.013). In addition your sample size is very low and correlation between clinical efficacy and e-field may be biased by outliers (figure 3).
On 2021-07-01 12:29:35, user ad4 wrote:
Very interesting study.
One thing that must be considered is whether passports/certificates will push groups in high outbreak probability regions further away from vaccinating. That seems to be the case for the UK: see below for a study I recently conducted in the UK.
On 2020-08-18 08:04:09, user Christian Fritze wrote:
Compare the probability of infection in line 262 with the statement starting line 336. So then we are to conclude that traveling on a 2 hr flight gives us a similar probability of infection as the average American does spending 2 hrs on other average activities. At the present time, the pandemic is not under control in the US, so we would want to be participating in activities that have far lesser risk of infection than average US behaviors presently have. Thus flying is not a responsible choice.
On 2020-12-08 11:30:27, user Abdur Rahman wrote:
This preprint is accepted for publication, and the new link is: <br /> https://onlinelibrary.wiley...
On 2021-04-28 13:32:34, user Huijghebaert Suzanne wrote:
This study poses a number of relevant questions to resolve, before concluding on efficacy. To start: calculating backwards, the number of PCR negative symptomatic subjects (31 in total) were comparable in both treatment groups, suggesting that all differences over the 21 days of treatment were accounted for solely by COVID-19, so strangely, the spray – in contrast to what is claimed – would not prevent other common colds....! So, how much analytical flaw, how much efficacy?<br /> 1) Calculating the % back in carrageenan+saline versus saline alone 7.6% corresponds to 15 and 8.6%<br /> to 17 subjects each respectively, totaling 32 instead of 31: where is the incorrect overlap, 1 person too<br /> much calculated as +?<br /> 2) Most importantly, which PCR test did you use and how did you assure that carrageenan did not interfere with the PCR assay? Cfr Ribeiro 18th Apr, 2013, asking for a safe way to remove carrageenan from RNA samples and reporting that after RNA extraction (by Trizol), reverse transcription and real time PCR, only the control group (saline injected, without carrageenan) had positive amplification, while carrageenan interfered<br /> with the reaction. <br /> 3) The impact of carrageenan may additionally also already interfere at the sampling stage by affecting/reducing the RNA loading, leading to less<br /> positive results (cfr Laurie et al.). Also that step should be validated.<br /> 4) As the difference moreover is very small, and the outbreaks in health care personal often concerns clusters within a same division, how do the individual data relate to different clusters within given departments?
Without proper profound validation the PCR test(s) in presence of various carrageenan concentrations, the findings may not translate in a true benefit, but possibly mask (so missed) viral positivity and so falsely hide transmission events. Unless validated in detail, the combination of already reported reduced PCR-response (due to carrageenan) in the nasal samples and case clustering may possibly annihilate all differences of significance.
Suzy Huijghebaert, Belgium
On 2021-10-12 14:55:07, user Ho Hum wrote:
The study was based on vaccination's done over a 2 month period. I could see that 32K might be too low but 800K seems too high considering that the entire population of Ottawa is just under 1 million. Did they vaccinate 80% of the population of Ottawa in just two months?
Something very wrong here.
The 1 in 1000 could be valid for the age 16-25 group. I could see a total of 32K of this demo being vaccinated in two months.
Too bad the researchers lost all their credibility over bad math
On 2021-12-30 08:12:32, user J Sato wrote:
Could you breakdown the risk of hospitalization/death by vaccination status/time?
On 2021-11-15 05:09:26, user John Davies wrote:
Might be good to make it extra clear in plain English that these are background rates - not actual vaccine side effects, as they appeared to me at first glance.
Other lay people might use these data to perpetrate the antivax argument.
On 2020-04-17 14:41:47, user the.plummers@talktalk.net wrote:
Note that the link to the model on the front of the paper is out of data. It should be https://github.com/arp23/Ba... . The link on this medRxiv page is correct. Thanks.
On 2020-07-04 02:28:38, user Experimental Methods wrote:
Do you have any breathability (differential pressure) data for this combination? It is easy to get high filtration efficiency, but if the material isn't breathable enough, most air inhaled will come from the side of the mask, defeating its purpose
On 2020-04-09 07:07:30, user Daniel Corcos wrote:
This calculation is made by dividing the number of deaths by the number of days since the FIRST Covid death, and by comparing to the number of deaths per day by driving during the same period. This means that the death rate by day is diluted with the period of low mortality per day from Covid.
But the highest mortality per day is yet to come. And, already, social distancing has been implemented and has some effect.<br /> This means that there is no evidence to support the hypothesis that there is overreaction to the pandemic.
On 2020-04-14 08:52:28, user The Cultic Experience wrote:
I'm trying to figure out your logic here. Social distancing alleviates the overcrowding in ICU? How does a clear social engineering exercise actually alleviate the pressure put on ICU in hospitals exactly?
On 2021-10-07 22:22:53, user Robyn Schofield wrote:
"As the devices do not meet medical device electrical safety standards (EN60601) they were operated at a distance of >=1.5metres from any patient." Can the authors please clarify - what wavelength the UV was operating at, and whether this device has been tested for ozone production / loss rates. I assume that the EN60601 requires ozone production to be tested for? If ozone is being produced (or destroyed to odd oxygen) that this would need testing before deployment in a medical setting. Ozone, a respiratory irritant gas, will easily travel more than 1.5m (so distance should not be seen as useful in setting safety protocols for electronic air cleaning devices in a medical setting).
Are hospital rooms with no ventilation in line with current infection prevention and control or hospital design / operational guidelines in the UK? In Australia this would be in breach of both our hospital design and operating guidelines which require a minimum of 6 ACH for all hospitals.
The effectiveness of UV with air high flow rates has to be questioned (because the exposure time for bio-aerosols is short) - are the authors able to separate the effectiveness of the filtration over the UV features? Most literature on this point shows that in real-world operation the HEPA provides 99.97% of the removal of bio-aerosols from air and the advantage of UV is untested / unproven (this is particularly true at 1000m3/h flow rates this device is operating at). I assume this device will be noisy >65dB - can this please be specified.
On 2020-06-06 15:50:28, user Alberto M. Borobia wrote:
Dear Authors, congratulations for your publication. Your reference Borobia et al. is now published in JCM.
Borobia, A.M.; Carcas, A.J.; Arnalich, F.; Álvarez-Sala, R.; Monserrat-Villatoro, J.; Quintana, M.; Figueira, J.C.; Torres Santos-Olmo, R.M.; García-Rodríguez, J.; Martín-Vega, A.; Buño, A.; Ramírez, E.; Martínez-Alés, G.; García-Arenzana, N.; Núñez, M.C.; Martí-de-Gracia, M.; Moreno Ramos, F.; Reinoso-Barbero, F.; Martin-Quiros, A.; Rivera Núñez, A.; Mingorance, J.; Carpio Segura, C.J.; Prieto Arribas, D.; Rey Cuevas, E.; Prados Sánchez, C.; Rios, J.J.; Hernán, M.A.; Frías, J.; Arribas, J.R.; on behalf of the COVID@HULP Working Group. A Cohort of Patients with COVID-19 in a Major Teaching Hospital in Europe. J. Clin. Med. 2020, 9, 1733.
Best regards,
On 2024-04-26 17:02:43, user Gary Goldman wrote:
We broadened our analyses (of IMRs) to explore potential relationships between childhood vaccine doses and NMRs (neonatal mortality rates) and U5MRs (under age 5-year mortality rates). Using 2019 and 2021 data, 17 of 18 analyses (12 linear regressions and six ANOVA and Tukey-Kramer tests) achieved statistical significance and corroborated the trend reported in our original study, demonstrating that as developed nations require more vaccine doses for their young children, mortality rates worsen. Please see https://pubmed.ncbi.nlm.nih...
On 2020-04-08 01:57:26, user Eliot Abrams wrote:
This just fits a gaussian curve. Absurd. Among other reasons, there is a second wave as soon as the current shelter in place restrictions are lifted.
On 2021-12-28 17:10:57, user madmathemagician wrote:
Small whole numbers, like "daily new cases and deaths", are not even expected to obey Benfords' distribution.
Using that to "scientifically" cast doubt about the reliability of EU COVID-19 data, is just fraudulent science.
The conclusion that higher vaccination rates correlates with larger deviation from Benfords' distribution is probably just because the "daily new cases and deaths" are smaller numbers.
On 2021-07-14 16:34:10, user Melissa Mallon wrote:
I was just doing research and found this article and this describes my nose sensation. It feels like I just used a nose spray and it is clear and dry. It is a little worrisome for sure. I was in Mexico and tested positive for Covid. Small cough, smell gone, headache that's it and now this nose spray feeling. I am about 7 days after test and probably about 10 days since first symptoms of cough and headache. By the way I was fully vaccinated.
On 2021-01-05 14:01:12, user Lianna Martin wrote:
Hiya - I had suspected covid 11th March and still can barely smell. My nasal passage more recently has become painfully crusted up coinciding with most things with a strong smell coming across like bleach or petrol to me. Taste comes and goes. I can live with symptoms, but would love to be part of a study...
On 2025-08-26 09:25:30, user Constant VINATIER wrote:
Feedbacks about your preprint : https://doi.org/10.1101/2025.07.31.25332504
About registration: <br /> We could not find any information about the pre-registration of your study in the pre-print. Pre-registration involves documenting the hypotheses, methods, and/or analyses of a scientific study prior to its conduct (10.1073/pnas.1708274114; 10.1038/s41562-021-01269-4). If your study was pre-registered, we strongly encourage you to include the registration number in the pre-print, ideally in the abstract make this important information easy to retrieve, as this practice enhances transparency and reproducibility. If the study was not pre-registered, this should be acknowledged as a limitation. For future studies, we recommend pre-registering on an appropriate repository.<br /> About Protocol Sharing: <br /> We did not find the protocol for your study. If you have one, we encourage you to share it as supplementary material or deposit it in a publicly available repository such as the Open Science Framework ( https://osf.io ) or Zenodo ( https://zenodo.org/) "https://zenodo.org/)") . You can then include a statement in the Methods section indicating that your protocol is openly available (e.g., 'The protocol for this study is available at (link)/ in the supplementary'). Sharing your protocol will help readers better understand your study and enable them to reproduce it if they wish to test it.<br /> About the Statistical Analysis Plan Sharing: <br /> We did not find the Statistical Analysis Plan (SAP) for your study. If you have one, we encourage you to share it as supplementary material or deposit it in a repository such as the Open Science Framework ( https://osf.io ). You can then include a statement in the Methods section indicating that your protocol is openly available (e.g., 'The SAP for this study is available at (link)/ in the supplementary'). Sharing your SAP will help readers better understand your study and enable them to reproduce it if they wish to test it.<br /> About Deviations and/or changes<br /> We could not find any information about potential deviations or changes to the protocol in your pre-print. Since such deviations are common, if this applies to your study, we strongly encourage you to include a subsection titled Changes to the Initial Protocol in the Methods' section and discuss these changes as a potential limitation of your results. If any deviations occurred during your study, please specify them in this new subsection.<br /> About Data sharing / FAIR Data<br /> We found insufficient information about your data sharing approach. Data should be findable, i.e. data are assigned a globally unique and persistent identifier (for instance there is a DOI assigned to the dataset, or data are registered or indexed in a searchable resource). Data should also be accessible, i.e. data are retrievable by their identifier and can be accessed following an open, free, and universally implementable protocol. The protocol allows for an authentication and authorization procedure, where necessary. As your data contains sensitive data, we suggest to make it Findability, Accessibility, Interoperability, and Reuse ( https://www.go-fair.org/fair-principles/) "https://www.go-fair.org/fair-principles/)") by providing some details on this procedure.<br /> About Code sharing<br /> We could not find any information about your (statistical) code. Sharing code is important for enhancing transparency and reproducibility, especially since it does not contain sensitive information. We encourage you to openly share it on a code sharing platform (Github, Codepen, CodShare, etc.) and include the Digital Object Identifier (DOI) in the Methods section. If you want more information about Code sharing https://fair-software.nl/ .<br /> Comments :<br /> Dear authors,<br /> You did not publicly share your data but adequately justified why (confidentiality of data from patient records) and clearly explained the procedure for obtaining it, thank you. But is it possible to openly share the code ?
On 2021-05-10 09:54:41, user Auroskanda Vepari wrote:
Do the findings suggest that patients who suffered a natural infection resulting in detectable anti-spike antibodies do not necessarily require a single or double does of a vaccine?
On 2020-06-03 17:13:16, user Euclides Castilho wrote:
cases according the results of the survey is not the same that the official statistcs say. These are cases according the surveillance definition . Official statistics do not take in account "infected" people
On 2020-03-29 18:14:17, user Sinai Immunol Review Project wrote:
Main findings:
This study examined<br /> the incidence of diarrhea in patients infected with SARS-CoV-2 across three<br /> recently published cohorts and found that there are statistically significant differences<br /> by Fisher’s exact test. They report that this could be due to subjective<br /> diagnosis criterion for diarrhea or from patients first seeking medical care<br /> from gastroenterologist. In order to minimize nosocomial infections arising<br /> from unsuspected patients with diarrhea and gain comprehensive understanding of<br /> transmission routes for this viral pathogen, they compared the transcriptional<br /> levels of ACE2 of various human tissues from NCBI public database as well as in<br /> small intestine tissue from CD57BL/6 mice using single cell sequencing. They<br /> show that ACE2 expression is not only increased in the human small intestine,<br /> but demonstrate a particular increase in mice enterocytes positioned on the<br /> surface of the intestinal lining exposed to viral pathogens. Given that ACE2 is<br /> the viral receptor for SARS-CoV-2 and also reported to regulate diarrhea, their<br /> data suggests the small intestine as a potential transmission route and<br /> diarrhea as a potentially underestimated symptom in COVID19 patients that must<br /> be carefully monitored. Interestingly, however, they show that ACE2 expression<br /> level is not elevated in human lung tissue.
Limitations of the Study:
Although this study demonstrates a statistical<br /> difference in the incidence of diarrhea across three separate COVID19 patient<br /> cohorts, their conclusions are limited by a small sample size. Specifically,<br /> the p-value computed by Fisher’s exact test is based on a single patient cohort<br /> of only six cases of which 33% are reported to have diarrhea, while the<br /> remaining two larger cohorts with 41 and 99 cases report 3% and 2% diarrhea incidence,<br /> respectively. Despite showing significance, they would need to acquire larger<br /> sample sizes and cohorts to minimize random variability and draw meaningful conclusions.<br /> Furthermore, they do not address why ACE2 expression level is not elevated in<br /> human lung tissue despite it being a major established route of transmission<br /> for SARS-CoV-2. It could be helpful to validate this result by looking at ACE2<br /> expression in mouse lung tissue. Finally, although this study is descriptive<br /> and shows elevated ACE2 expression in small intestinal epithelial cells, it<br /> does not establish a mechanistic link to SARS-CoV-2 infection of the host.<br /> Overall, their claim that infected patients exhibiting diarrhea pose an increased<br /> risk to hospital staff needs to be further substantiated.
Relevance:
This study provides a possible transmission route and a potentially underappreciated<br /> clinical symptom for SARS-CoV-2 for better clinical management and control of<br /> COVID19.
On 2020-04-19 17:16:34, user David Steadson wrote:
I now realise I made an error in the last comment, taking the cumulative Swedish total instead of the Stockholm total. On April 1 FHM reported 148 deaths for Stockholm. Given delays in reporting 200 may be a reasonable guess of the actual numbers then. Unfortunately FHM only retroactively updates national totals, not regional
On 2021-07-03 11:55:18, user Todd Gothard wrote:
This work could be advanced by elaborating the relation to what is referred to as contact rate elsewhere. Heesterbeek "The saturating contact rate in marriage and epidemic models" (J Math Biol 31, 1993) is related.
On 2023-03-29 21:17:46, user Kevin J. Black wrote:
The final published version appears at https://www.mdpi.com/2077-0...
On 2021-01-15 14:29:13, user Serge Richard wrote:
Would you please inform the financial Interest Links between these authors and the pharmaceutical compagnies involved in the drugs refered to ?
On 2021-08-25 00:35:16, user Andrew Huang wrote:
Can I check, if my body weight is 67 kg, I need to take : Honey 67gms per day Nigella Sativa - 12,060mg = 80mg/day ? Quite a large amount of honey based on this.
On 2020-04-06 19:30:01, user Xavier de Roquemaurel wrote:
Could you please run the same analysis splitting countries which use the Tokyo-172 strain (Japan, Taiwan, Malaysia) from the other countries? This strain dates 1924 and has been less modified than the strains used in other countries like France for example. Thanks. Xavier
On 2021-10-30 19:49:57, user Sanghyuk Shin wrote:
Congrats to the authors on this monumental effort. However, the paper will be much stronger by drawing from the extensive literature on racism and its impact on mistrust of health system among Black and other minoritized people as summarized in https://www.healthaffairs.o...
On 2020-04-16 03:43:06, user Rhodes wrote:
Why 600 mg dose? (Compare Sermo international barometer showing most doctors prescribing 400 mg) Why no supplements? (zinc, vitamin c, d, copper).
On 2024-07-05 14:17:45, user Benjamin Glemain wrote:
Published article for this pre-print: https://www.nature.com/arti...
On 2022-02-09 05:48:27, user kdrl nakle wrote:
This is totally outdated. Samples from May to October 2020. I wondered why the dates were not put in abstract, obviously the authors knew this would be immediate complain. You are reporting on a virus with 2-3 R_0 while we are now dealing with virus on 10+ R_0 which is almost certainly spreading by aerosols. Your results have no bearings on any recommendation for the current VOC.
On 2025-02-28 22:17:07, user Brian wrote:
I’m a nobody, however I’m able to use the resources which are at my disposal to better understand this study. I have constructed the following logical explanation. I thoroughly invite anyone to dismantle this explanation. It is to the best of my knowledge and understanding that I’ve created this.
It found that CD4 T cells are reduced, and TNFa producing CD8 T cells are increased. It found that cDC2 cells were reduced while non classical monocytes were elevated. To also include that elevated cytokines and IgG subclass shifts did not occur. In a healthy immune system, elevated cytokines and IgG subclass shifts indicate a healthy immune response. Furthermore, a reduction of cDC2 cells means that without sufficient numbers of cDC2 cells, the body struggles to activate T cells effectively, which is key for a strong immune response. Next, elevated non classical monocytes means that the body is in a state of immune activation, but instead or responding efficiently to the threat (due to a lack of other immune cells like cDC2 cells), the system is stuck in a more passive or inflammatory state. And let’s not forget AIDS is characterized by a reduction of CD4 T cells and elevated TNFa-producing CD8 T cells. I rest my case.
On 2020-04-25 22:54:50, user wbgrant wrote:
This open access article supports the present modeling study and should be cited in the final version:<br /> Low Temperature and Low UV Indexes Correlated with Peaks of Influenza Virus Activity in Northern Europe during 2010?2018.<br /> Ianevski A, Zusinaite E, Shtaida N, Kallio-Kokko H, Valkonen M, Kantele A, Telling K, Lutsar I, Letjuka P, Metelitsa N, Oksenych V, Dumpis U, Vitkauskiene A, Stašaitis K, Öhrmalm C, Bondeson K, Bergqvist A, Cox RJ, Tenson T, Merits A, Kainov DE.<br /> Viruses. 2019 Mar 1;11(3). pii: E207. doi: 10.3390/v11030207.<br /> http://www.mdpi.com/resolve...
On 2020-04-14 21:47:58, user Dr Eric Grossi Neurocirurgia wrote:
I would like to highlight a serious methodological error in this study. What we want for a drug treatment of COVID-19, only two objectives, to avoid and / or treat SARS and reduce contagion, therefore pragmatism in the selection of patients must be as close as possible to the clinical reality, which did NOT occur, since only patients between the ages of years were analyzed. This alone invalidates any useful result, since the vast majority of human losses are over the age of 64.
https://uploads.disquscdn.c... <br /> (Registry in ChinaClinicalTrialRegistry site) - look the date of approved by ethic commitee 5 day after the begun of study, and look too the final date - isn't a preview release NO NO , the study is OVER
On 2022-01-08 00:16:51, user Claudia Lupu wrote:
Excellent article, many of my friends Vax and no Vax got Covid, some fully vaxed had more severe symptoms that non vaxed because of their medical condition. This reinforce exactly what is said in the article.
On 2022-01-17 00:08:52, user Satvinder Singh Bawa wrote:
The key findings of this study are identical to the South California study. Vaccines are effective for Delta. Not for Omicron. Infection rates are actually highest in the boosted. But hospitalizations are lowest in boosted.<br /> https://www.medrxiv.org/con...
On 2020-06-24 18:56:17, user André GILLIBERT wrote:
Title : Proposal for improved reporting of the Recovery trial<br /> André GILLIBERT (M.D.)1, Florian NAUDET (M.D., P.H.D.)2<br /> 1 Department of Biostatistics, CHU Rouen, F 76000, Rouen, France<br /> 2 Univ Rennes, CHU Rennes, Inserm, CIC 1414 (Centre d’Investigation Clinique de Rennes), F- 35000 Rennes, France
**Introduction**
Dear authors,<br /> We read with interest the pre-print of the article entitled “Effect of Dexamethasone in Hospitalized Patients with COVID-19: Preliminary Report”. This reports the preliminary results of a large scale randomized clinical trial (RCT) conducted in 176 hospitals in the United Kingdom. To our knowledge it is the largest scale pragmatic RCT comparing treatments of the COVID-19 in curative intent. The 28-days survival endpoint is objective, clinically relevant and should not be influenced by the measurement bias that may be caused by the open-label design. While 2,315 study protocols have been registered on ClinicalTrials.gov about COVID-19, as of June 24th 2020, Recovery is, to our knowledge, the only randomized clinical trial on COVID-19 that succeeded to include more than ten thousands patients. The open-label design and simple electronic case report form (e-CRF) may have helped to include a non-negligible proportion of all COVID-19 patients hospitalized in the United Kingdom (UK). Indeed, as of June 24th 2020, approximatively 43,000 patients died of COVID-19 in hospital in the UK, of whom approximatively 0.24 × 11,500 = 2,760, that is more than 6% of all hospital deaths of COVID-19, where included in the Recovery study.<br /> Having read with interest version 6.0 of the publicly available study protocol (https://www.recoverytrial.n... "https://www.recoverytrial.net/files/recovery-protocol-v6-0-2020-05-14.pdf)") we had hoped for more details in the reporting of methods and results of this trial and take advantage of the open-peer review process offered by pre-prints servers to suggest improving some aspects of the reporting before the final peer-reviewed publication. Please, find below some easy to answer comments that may help to improve the article overall.
**Interim analyses and multiple treatment arms**
The first information would be about interim analyses. The protocol (version 6.0) specifies that it is adaptive and that randomization arms may be added removed or paused according to decisions of the Trial Steering Committee (TSC) basing its decision on interim analyses performed by the Data Monitoring Committee (DMC) and communicated when “the randomised comparisons in the study have provided evidence on mortality that is strong enough […] to affect national and global treatment strategies” (protocol, page 16, section 4.4, 2nd paragraph). The Supplementary Materials of the manuscript specifies that “the independent Data Monitoring Committee reviews unblinded analyses of the study data and any other information considered relevant at intervals of around 2 weeks”. This suggests that many interim analyses may have been performed from the start (March 9th) to the end (June 8th) of the study.<br /> Statistically, interim analyses not properly taken in account generate an inflation of the type I error rate which may be increased again by the multiple treatment arms. Methods such as triangular tests make it possible to control the type I error rate. Most methods of control of type I error rate in interim analyses require that the maximal sample size be defined a priori and that the timing and number of interim analyses be pre-planned. This protocol being adaptive, new arms were added, implying new statistical tests in interim analyses, and no pre-defined sample size as seen in page 2 of the protocol: “[...] it may be possible to randomise several thousand with mild disease [...], but realistic, appropriate sample sizes could not be estimated at the start of the trial.” This make control of the type I error rate difficult. The fact that the study has been stopped on the final analysis as we understand from the current draft rather than interim analysis does not remove the type I error rate inflation. The multiple treatment arms lead to another inflation of the type I error rate.<br /> The current manuscript does not specify any procedure to fix these problems. The Statistical Analysis Plans (SAP) V1.0 (in section 5.5) and V1.1 (in section 5.6) specify that “Evaluation of the primary trial (main randomisation) and secondary randomisation will be conducted independently and no adjustment be made for these. Formal adjustment will not be made for multiple treatment comparisons, the testing of secondary and subsidiary outcomes, or subgroup analyses.” and nothing is specified about interim analysis. Therefore, we conclude that no P-value adjustment for multiple testing has been performed, neither for multiple treatment arms nor for interim analysis. If an interim analysis assessing 4 to 6 treatment arms at the 5% significance level has been performed every 2 weeks from march to June, up to 50 tests may have been performed, leading to major inflation of type I error rate. In our opinion, the best way to assess and maybe fix the type I error rate inflation, is to report with maximal transparency every interim analysis that has been performed, with the following information:<br /> 1. Date of the interim analysis and number of patients included at that stage<br /> 2. Was the interim analysis planned (e.g. every 2 weeks as planned according to supplementary material) or unplanned (e.g. due to an external event, for instance the article of Mehra et al about hydroxychloroquine published in The Lancet, doi:10.1016/S0140-6736(20)31180-6), and if exceptional, why?<br /> 3. Which statistical analyzes, on which randomization arms, have been performed at each stage <br /> 4. If predefined, what criteria (statistical or not) would have conducted to early arrest of a randomization arm for inefficiency and what criteria would have conducted to arrest for proved efficacy?<br /> 5. If statistical criteria were not predefined, did the DMC provide a rationale for his choice to communicate or not the results to the TSC? If yes, could the rationale be provided?<br /> 6. The results of statistical analyzes performed at each step<br /> 7. The decision of the DMC to communicate or not the results to the TSC and which results have been reported as the case may be<br /> The information about interim analyses and multiple randomization arms will help to assess whether the inflation of type I error rate is severe or not. A post hoc multiple testing adjustment, taking in account the many randomized treatments and interim analyses, should be attempted, and discussed, even though there may be technical issues due to the adaptative nature of the protocol.
**Adjustment for age**
An adjustment for age (in three categories <70 years, 70-79, >= 80 years, see legend of table S2) in a Cox model was performed for the comparison of dexamethasone to standard of care in the article. This adjustment was not specified in the version 6.0 of the protocol but was, according to the manuscript “added once the imbalance in age (a key prognostic factor) became apparent”. This is confirmed by the addition of a words ““However, in the event that there are any important imbalances between the randomised groups in key baseline subgroups (see section 5.4), emphasis will be placed on analyses that are adjusted for the relevant baseline characteristic(s).” in section 5.5 page 16 of the SAP V1.1 of June 20th compared to the SAP V1.0 of June 9th which specified a log-rank test. The SAP V1.0 of the 9th June may have been written before the database has been analyzed (data cut June 10th) but the SAP of the 20th has probably been written after preliminary analysis have been performed. This is consistent with the words “became apparent” of the manuscript. Therefore, in our opinion, this adjustment must be considered as a post hoc analysis rather than as the main analysis. Moreover, even though the SAP V1.1 specifies that an “important imbalance” will lead to an “emphasis” on adjusted analyses, it does not change the primary analysis (see section 5.1.1 page 14). It is not clear what “important imbalance” means. To interpret that, we will perform statistical tests to assess balance of key baseline subgroups specified in SAP V1.1 (see section 5.4):<br /> 1. Risk group (three risk groups with approximately equal number of deaths based on factors recorded at randomisation). Its distribution is shown in figure S2. A chi-square tests on the distribution of risk groups in Dexamethasone 1255/500/349 and Usual care 2680/926/715 groups, lead to a P-value=0.092. A chi-square test for trend yields a P-value equal to 0.23.<br /> 2. Requirement for respiratory support at randomisation (None; Oxygen only; Ventilation or ECMO). P-value=0.89 for chi-square test and P-value=0.86 for chi-square for trend.<br /> 3. Time since illness onset (<=7 days; >7 days). P-value=0.17<br /> 4. Age (<70; 70-79; 80+ years). P-value=0.016 for chi-square test, p=0.019 for chi-square test for trend<br /> 5. Sex (Male; Female). P-value=0.97 for chi-square test<br /> 6. Ethnicity (White; Black, Asian or Minority Ethnic). No data found.<br /> The criteria to define “important imbalance” seems to be statistical significance at the 0.05 threshold, however that should have been stated and tests for all other variables should have been provided too.<br /> First, this adjustment, from a theoretical point-of-view, was not necessary since the study was randomized; if the exact condition of imbalance triggering the adjustment was pre-specified in the protocol or SAP before the imbalance was known, it could induce a very slight reduction of the type I error rate and power. However, as it was performed when the imbalance was known, there is a risk that the sign of the imbalance (i.e. higher age in the dexamethasone group) have influenced the choice of adjustment. Indeed, an adjustment conditional to a higher age in the dexamethasone group will increase the estimated effect of dexamethasone in these conditions, and so, provide an inflation of the type I error rate. If the same conditional adjustment were further considered for other prognostic variables, the inflation could even be higher. <br /> Unless there is strong evidence that the amendment to the SAP was performed without knowledge of the sign of the imbalance (higher age in the dexamethasone group), we suggest that the primary analysis be kept as originally planned, without adjustment, and that the age adjustment be performed in a sensitivity analysis only. The knowledge of the sign of the unbalance is unclear in the last version of the SAP (V1.1, June 20th) and in the manuscript. In addition, in an open label trial, it is always better to stick to the protocol.
**Results in other treatment arms**
The manuscript specifies that “the Steering Committee closed recruitment to the dexamethasone arm since enrolment exceeded 2000 patients.” It is not stated whether any other treatment arm has exceeded 2000 patients or not and whether the study is still ongoing. Results of treatment arms that have been stopped should be provided (all arms having enrolled more than 2000 patients?). If not, the number of patients randomized in other treatment arms should, at least, be reported. If the study is completely stopped, all treatments should be analyzed and reported, unless there is a specific reason not to do so; that reason should be stated as the case may be. This data would be useful to provide evidence on other molecules. It would also clarify the number of statistical tests that have been performed or not, providing more information about the overall inflation of alpha risk.
**Sample size**
The paragraph about the sample size suggests that inclusions were planned, at some time, to stop when 2000 patients were included in the dexamethasone arm. The amended protocol (May 14th), the SAP V1.0 (June 9th) and the SAP V1.1 (June 20th, 4 days after the results have been officially announced) all have a paragraph about the sample size but all specify that the sample size is not fixed and none specify any criteria of arrest of the research based on sample size. There are 2104 patients included in this arm, which is substantially larger than the target of 2000 patients. The exact chronology and methodology should be clarified: when was the sample size computed and what was the exact criteria to arrest the research? Could the document (internal report?) related to this sample size calculation and statistical or non-statistical decision of arrest of the research be published in supplementary material?<br /> Indeed, assessment of the type I error rate requires knowing exactly when and why the research has been arrested: arrest for low inclusion rate of new patients or for reaching target sample size cannot be interpreted the same as arrest for high efficacy observed on an interim analysis.
**Future of the protocol**
With the new evidence about dexamethasone, the protocol will probably be stopped or evolve. The future recruitment may slow as the peak of the epidemic curve in United Kingdom is passed. The past, present and future of the protocol needs also to be known to assess the actual type I error rate. Indeed, future analyses, that have not yet been performed influence the overall type I error rate. That is why we suggest that author’s provide the daily or weekly inclusion rate from March to June and discuss the future of the study.
**Loss to follow-up**
Table S1 shows that the follow-up forms have been received for 1940/2104 (92.2%) patients of the dexamethasone group and 3973/4321 patients of the usual care group (91.9%). The patients without follow-up forms (8.5% overall) may either be lost to follow-up or have been included in the 28 last days before June 10th 2020 (data cut). The manuscript mentions that 4.8% of patients “had not been followed for 28 days by the time of the data cut”, suggesting that 8.5%-4.8% = 3.7% of patients are lost to follow-up, but that is our own interpretation. We suggest that authors report the actual number of loss to follow-up and how their data have been imputed or analyzed. The number of loss to follow-up may differ for different outcomes. For instance, if the Office of National Statistics (ONS) data has been used for vital status assessment, there should be no loss to follow-up on that outcome.
**Vital status**
The current manuscript only specifies the data of the web-based case report (e-CRF) form, filled by hospital staff, as source of information, suggesting that it is the only source of information about the vital status. The document entitled “Definition and Derivation of Baseline Characteristics and Outcomes” provided at https://www.recoverytrial.n... specifies many other sources. For instance, the vital status had to be assessed from the Office of National Statistics (ONS). Other sources, including Secondary Use Service Admitted Patient Care (SUSAPC) and e-CRF could be used for interim analysis. The ONS was considered as the defining source (most reliable). Whether the ONS data has been used or not should be clarified. If the ONS data have been used, statistics of agreement of the two data sources (e-CRF and ONS) may be provided to help assessing the quality of data. If the ONS data have not been used, this deviation from the planned protocol should be documented.<br /> The manuscript as well as the recovery-outcomes-definitions-v1-0.pdf file specifies that the follow-up form of the e-CRF is completed at “the earliest of (i) discharge from acute care (ii) death, or (iii) 28 days after the main randomisation”. If the follow-up form is not updated further, patients discharged alive before day 28 (e.g. day 14) may have incomplete vital status information at day 28. The following information should be specified:<br /> 1. Whether the follow-up form of the e-CRF had to be updated by hospital staff at day 28 for these patients<br /> 2. If response to (1) is yes, whether there was a means to distinguish between a lost to follow-up at day 28 (form not updated) and a patient discharged and alive at day 28 (form updated to “alive at day 28”)<br /> 3. If response to (2) is yes, how many patients discharged before day 28 were lost to follow-up at day 28<br /> 4. If response to (2) is yes, how has their vital status at day 28 been imputed or managed in models with censorships (log-rank, Kaplan-Meier, Cox)<br /> Of course, this information is really needed if the ONS and SUSAPC data have not been used.<br /> The quality of the vital status information is critical in such a large scale open-label multi-centric trial, because there is a risk that one or more center selectively report death, biasing the primary analysis.
**Inclusion distribution by center**
A multicentric study provides stronger evidence than a single-center study but sometimes, few centers include most patients, with a risk of low-quality data or selection bias. The very high number of included patients in the Recovery trial suggests that many centers included many patients but the distribution of inclusions per center could be reported.
**Randomization**
The protocol specifies that “in some hospitals, not all treatment arms will be available (e.g. due to manufacturing and supply shortages); and at some times, not all treatment arms will be active (e.g. due to lack of relevant approvals and contractual agreements).” This is further clarified in the SAP V1 (section 2.4.2 Exclusion criteria, page 8) by the sentence “If one or more of the active drug treatments is not available at the hospital or is believed, by the attending clinician, to be contraindicated (or definitely indicated) for the specific patient, then this fact will be recorded via the web-based form prior to randomisation; random allocation will then be between the remaining (or indicated) arms.” Showing that randomization arms may be closed on an individual basis, when the patient is included, with the argument of contraindication or definitive indication. It seems that the “standard of care” group could not be removed and that at least another randomization arm had to be kept as suggested by the words “random allocation will then be between the remaining arms (in a 2:1:1:1, 2:1:1 or 2:1 ratio)” in section 2.9.1 page 11 of the SAP V1.0. Even exclusion of a single randomization arm can lead to imbalance between groups. For instance, if physicians believed that a treatment was contraindicated for the most severe patients, only non-severe patients could be randomized to the treatment’s arm, while most severe patients would be randomized to other arms. Several things can be done to assess and fix this bias. First, report how many times this feature has been used and which randomization arms have been most excluded. If it has been used many times, provide the pattern of use that help to assess whether this is a collective measure (e.g. 2-weeks period of shortage of a treatment in a center ? no major selection bias) or individual measure. If its use has been rare, a sensitivity analysis could simply exclude these patients. If it has been frequent, we suggest a statistical method to analyze this data without bias, based on the following principles: patients randomized between 3 randomization arms A, B and C (population X) are comparable for the comparisons of A to B. Patients randomized between A, B and D (population Y), are comparable for the comparisons of A to B. Population X and population Y may differ but, inside each population, A can be compared to B. Therefore, the within-X comparison of A to B and within-Y comparison of A to B are both valid and can be meta-analyzed to assess a global difference between A and B. This can be simply done with an adjustment on the population (X or Y) in a fixed effects multivariate model. Pooling of X and Y populations should not be performed without adjustment.<br /> A second problem with randomization exists although the dexamethasone arm is the least affected. Randomization arms have been added in this adaptative trial. When a new randomization arm is added, new patients may be randomized to this arm and fewer patients are randomized to other arms. Consequently, the distribution of dates of inclusion may differ between groups. This may have some impact on the mortality at two levels: (1) the medical prescription of hospitalization may have evolved as the epidemic evolved, with hospitalization reserved to most severe patients at the peak of epidemic and maybe wider hospitalization criteria at the start of epidemic and (2) evolution of patients included in the Recovery trial. Indeed, even if centers should have included as many patients as possible as soon as their inclusion criteria were met, it is possible that they have only included part of eligible patients and that this part evolved with time. This bias can be easily assessed and fixed: the curves of inclusions in the different arms and mortality rate in the Recovery trial can be drawn as a function of date (from March to June) and an adjustment on date of inclusion may be performed in a sensitivity analysis.
**Conclusion**
Recovery is the study with the best methodology that we have seen on COVID-19 treatments in curative intent and we salute the initiative of publishing transparently the protocol, its amendments, the statistical analysis plan and the first draft of the report. We hope that our reporting suggestions will be taken in account in the final version of the paper. We think that discussing these points will qualify the interpretation of results, further improve the transparent approach adopted by designers of the study and improve the reliability of the conclusions. We expect a high-quality reporting of these final results, with full transparency on interim analyses, statistical analysis plans and statistical analysis reports. We hope that these comments are helpful and again we acknowledge that this study is not solely outstanding in terms of importance of the results but is also a stellar example for the whole field of therapeutic research. We invite other researchers to provide comments to this article to engage in Open Science.
On 2022-07-28 10:00:11, user Martin Schulte-Rüther wrote:
An updated, peer-reviewed version of this manuscript has been published in the Journal of Child Psychology and Psychiatry.
Schulte-Rüther M, Kulvicius T, Stroth S, Wolff N, Roessner V, Marschik PB, Kamp-Becker I, Poustka L (2022). Using machine learning to improve diagnostic assessment of ASD in the light of specific differential and co-occurring diagnoses. Journal of Child Psychology and Psychiatry. https://doi.org/10.1111/jcpp.13650
On 2020-07-05 14:02:12, user Nataliya Skrypnyk wrote:
It would be interesting to see the actual range of IL-13 in the both groups of patients
On 2021-09-20 13:55:05, user mike marchywka wrote:
There is a lot tto be learned from the aging issues,
On 2020-06-19 22:15:27, user Michelle Kimple wrote:
Have you thought of performing analyses of your data by city/county size and/or population density? In the abstract you state "We did not find an association between county level prevalence of COVID-19 cases and face covering use" but when I limited the data to only counties with the 5 most populous cities, there appears to be a strong correlation. I just tweeted my analysis of your data (the county populations may not be what you used, but the city and county population ranks are correct): https://twitter.com/KimpleL...
On 2020-03-04 13:30:42, user Bìtao Qiu wrote:
I think there are some wrongly put numbers on Table 3 (page 35), e.g. n = 59 for Immunodeficiency patients. It should be 3 according to the abstract and n = 3 on page 36.
On 2020-07-15 07:10:13, user Dr Ahmed Sayeed wrote:
Section Review comments and notes Abstract, title and references The study appears to be new and promising in the current scenario of COVID pandemic In the objectives, the authors have the aim to describe the bronchoscopic findings in COVID patients but in the method, they have forgotten to mention how the bronchoscopic findings will be studied What is the meaning of COVID19 patients? Is suspected covid19 or confirmed COVID 19 with Nasopharyngeal swab(PCR or serology or Nuclear acid amplification test) The references are recent and relevant with the inclusion of appropriate study
Introduction/background In introduction line 4, the term bronchial alveolar lavage would be more appropriate than bronchial culture The author uses the term culture repeatedly which excludes other methods like PCR, grams stain, KOH stain, AFB and would be advised to use the broader term to include other methods of detection of organisms The limitations of the study are not mentioned Methods The study subjects The age group of the patients should be mentioned and the site of covid infection? lung also needs to be mentioned The variables are defined and measured Yes the study appears to valid and reliable
Results My knowledge of statistics is very limited and it is difficult for me to comment
Discussion and Conclusions<br /> There is a grammatical error in line 2 and 5 of the discussion Suggest difficult to do suction In paragraph 3 of the discussion the reference 18 is written twice The reference in the discussion are not quoted in serial order The limitations of the study need to be explained more
Overall The study design was appropriate This study added the to the scarcity of the novel virus literature and it showed that more hospital acquired infections are common in patients with covid I did not find any major flaws in the article
full review:
Overall statement or summary of the article and its findings
The article needs some correction and rewriting with some of my suggestion<br /> Some more literature needs to be done and added to the discussion with some new references
Overall strengths of the article and what impact it might have in the respiratory field
The article appears to be promising and will definitely add to the literature of BAL in COVID which not frequently performed in fear of spreading the infection to the health care staff Culture and sensitivity will make a difference in the management of COVID ventilated patients
Specific comments on the weaknesses of the article and what could be done to improve it Major points in the article which need clarification, refinement, reanalysis, rewrites and/or additional information and suggestions for what could be done to improve the article.
More literature review<br /> More references need to be added<br /> Minor points like figures/tables not being mentioned in the text, a missing reference, typos, and other inconsistencies.
English and grammar
On 2021-12-23 17:28:29, user Heather Madden wrote:
Thank you for conducting this study, we were not included but as a family with a child with a ANKRD11 missense mutation that had never been seen before in a highly conserved region I was excited to see something written. I've actually had a Dr tell me missense mutations are harmless when they can pathogenic. My daughters half sister is still struggling to get a formal KBG dx, our mutation was proven pathogenic but the Dr used a different lab which did not have that data so its listed as a VUS. Frustrating for missense families, hopefully as more research is done other families won't need to go though long waits for answers.
On 2021-03-24 12:15:54, user Rogerblack wrote:
This studies depression and anxiety measure ASSUMES A HEALTHY PATIENT. 'little energy', 'trouble concentrating' 'moving slowly' = a minimum score of 3 due to physical symptoms of longcovid/fatigue. If very exhausted, this can easily rise into the 'severely depressed' range.
It is not unreasonable to use the PHQ-9 or similar as a screening measure of disease severity.
To use it in a patient population suffering from fatigue, concentration problems, ... is guaranteed to cross-read between those symptoms and anxiety - it is useless without a careful assessment of each question to find if you are measuring MH, or physical symptoms.
It absolutely cannot justify sentances such as "The physical, cognitive and mental health burden experienced by COVID-19 survivors was considerable. This included symptoms of anxiety and depression in a quarter" without much more work, as it will lead to the conclusion that treating depression may benefit the patient when there is no depression, and it's a scale artifact.
PHQ9 and similar scales are designed for patients without significant physical comorbidities to the mental state they are trying to measure. The normal scale cuts are only valid for this purpose.
I note similar concerns to those raised with the C-MORE paper. (https://www.medrxiv.org/con...
Edit: response to the promoter account on twitter in July raising this issue.
https://twitter.com/SithEle...<br /> '@PHOSP_COVID<br /> What is the current analysis plan and instruments (BDI,SF36) planned to be used to measure health? I am concerned that instruments can be misinterpreted and cross react between physical and mental health.'
On 2022-06-11 16:21:38, user Miles Markus wrote:
RHETORICAL QUESTION: Is there a possibility that drug-treatment-based estimates of "relapse" percentages (such as are given in the article) might not be entirely accurate? Just asking. SEE: Markus MB. 2022. Theoretical origin of genetically homologous Plasmodium vivax malarial recurrences. Southern African Journal of Infectious Diseases 37 (1): a369. https://doi.org/10.4102/saj....
On 2020-02-28 19:16:00, user Antoine Jomier wrote:
Hello, i am running an ai algorithm start up company. Would it be possible to share your model or data set so that we distribue it in France. We would not do any commercial exploitation but make it available widely to the community. My contact antoine.jomier@incepto-medical.com<br /> Thanks
On 2021-07-30 16:47:26, user Kirsten Elliott wrote:
There seem to be at least three reviews based on the same literature search, so I'm posting similar comments on each of them.
The search strategy for this review has not been adequately reported as there is not enough information in the appendix to replicate the search in any database, as it hasn't been made clear which fields were searched. It is therefore not compliant with PRISMA 2009 or PRISMA 2020 reporting standards. Furthermore, from what has been reported it appears that relevant search terms and subject headings have not been included, meaning that it is likely relevant papers have been missed.
On 2022-01-09 17:05:04, user rubenroa wrote:
Any reason for the increased Odd in vaccinated people against other studies in Israel which conclude: "Vaccination with at least two doses of COVID-19 vaccine was associated <br /> with a substantial decrease in reporting the most common post-acute <br /> COVID19 symptoms."https://www.medrxiv.org/con...
On 2021-01-21 16:36:21, user Michael wrote:
DL+DiReCT including the trained model is also available on github: https://github.com/SCAN-NRA...
This method is mentioned in the manuscript and refers to https://doi.org/10.1002/hbm...
On 2021-06-19 12:08:06, user Stephen Smith wrote:
That's just not accurate. The PEP studies have huge problems. Regarding the CID article, let me know when you find the supplemental information.<br /> Regarding our data, no one just doesn't "find" subgroups with this kind of difference in survival. BTW, can you explain the improvement of cumulative weight-based HCQ dose to cumulative HCQ dose? <br /> That kind of confirmation of a hypothesis is never just "found".
On 2021-07-26 04:09:55, user Matthew Robertson wrote:
“Our models estimate that nearly a third of COVID-19 cases would have been prevented if one of two exposures (diet and deprivation) were not present.”
The above sentence from the discussion section implies a causative relationship, but this study can not demonstrate causality, as has been correctly identified in the limitations section. In fact, it’s likely that socioeconomic deprivation (especially as it is measured in this study – postcode) is at least partially a surrogate indicator for other factors. Socioeconomic status is correlated with many things which could conceivably be more direct causes, for example: Vitamin D status[1], mental health[2], self-regulation[3] (and downstream effects there of), delayed gratification (even in people merely provided with environmental cues of poverty[4] ).
Also, only relative metrics are reported. Are you able to give any indication of where the sample/population diet scores sit in absolute terms, the HR of each additional serving of each food type (and plateau/high point), and/or describe the FFQ data (intra-quartile medians/distributions of each food)? I see the data that could inform the above is available, but given that there is an accessibility barrier to the data, it would be helpful to provide such granular information in an annex.
It is not only the use of a FFQ that reduces the resolution of the data, but also the use of an index to report and reduce the dataset to a single number. A plateau effect is not uncommon (for example the plateau in all-cause mortality observed at >5 servings of fruit/veg per day in one meta-analysis[5] ), but the point of plateau could also be the point at which the metric (index) ceases to have utility, and a refined, non-reductive or conditional-reasoning metric(s) continues to be useful. This point is highly significant in making any conclusions at all about the relative contribution of diet vs. socioeconomic status to Covid risk.
References
[1] Léger-Guist'hau J, Domingues-Faria C, Miolanne M, et al. Low socio-economic status is a newly identified independent risk factor for poor vitamin D status in severely obese adults. J Hum Nutr Diet. 2017;30(2):203-215. doi:10.1111/jhn.12405
[2] Isaacs AN, Enticott J, Meadows G, Inder B. Lower Income Levels in Australia Are Strongly Associated With Elevated Psychological Distress: Implications for Healthcare and Other Policy Areas. Front Psychiatry. 2018;9:536. Published 2018 Oct 26. doi:10.3389/fpsyt.2018.00536
[3] Palacios-Barrios, E. E., & Hanson, J. L. (2019). Poverty and self-regulation: Connecting psychosocial processes, neurobiology, and the risk for psychopathology. Comprehensive Psychiatry, 90, 52–64. https://doi.org/10.1016/j.comppsych.2018.12.012
[4] Liu L, Feng T, Suo T, Lee K, Li H. Adapting to the destitute situations: poverty cues lead to short-term choice. PLoS One. 2012;7(4):e33950. doi:10.1371/journal.pone.0033950
[5] Wang, X., Ouyang, Y., Liu, J., Zhu, M., Zhao, G., Bao, W., & Hu, F. B. (2014). Fruit and vegetable consumption and mortality from all causes, cardiovascular disease, and cancer: systematic review and dose-response meta-analysis of prospective cohort studies. BMJ : British Medical Journal, 349(jul29 3), g4490–g4490. https://doi.org/10.1136/bmj.g4490
On 2020-12-02 17:20:39, user Steven Luger wrote:
are there ways to furhter modify the input? Ex merv 8 filter?<br /> with full air turnover every 10 minutes but only 10% new outside air. <br /> ACH - does this refer to air change of recirculated indoor air passing through the merv 8? or does this mean full exchange of indoor air for outdoor air?
On 2024-09-23 09:53:00, user Carlos Carlos wrote:
How did you isolate the effect of vaccination in relation to other preventive measures?<br /> And generally, the countries that carried out the most efficient vaccinations were also those that used other preventive measures most efficiently.
On 2020-03-27 15:01:22, user Sinai Immunol Review Project wrote:
These authors looked at 17 hospitalized patients with COVID-19 confirmed by RT-PCR in Dazhou, Sichuan. Patients were admitted between January 22 and February 10 and the final data were collected on February 11. Of the 17 patients, 12 remained hospitalized while 5 were discharged after meeting national standards. The authors observed no differences based on the sex of the patients but found that the discharged patients were younger in age (p = 0.026) and had higher lymphocyte counts (p = 0.005) and monocyte counts (p = 0.019) upon admission.
This study is limited in the sample size of the study and the last data collection point was only one day after some of the patients were admitted.
These findings have been somewhat supported by subsequent studies that show that older age and an immunocompromised state are more likely to result in a more severe clinical course with COVID-19. However, other studies have been published that report on larger numbers of cases.
On 2021-10-01 15:38:22, user friidrottare wrote:
On 2022-01-21 14:18:29, user Rosanna wrote:
It will be interesting to know which organs are mostly affected by these autoantibodies. No mention about it is done in the manuscript. Do the authors have this information? Are lungs more affected in patients that suffered a critical COVID-19? <br /> Also, do the authors have data in male patients?
Rosanna Paciucci, Ph.D. Faculty Attending, Vall d'Hebron University Hospital, Barcelona, Spain
On 2025-09-06 18:58:16, user ??????? ?????? wrote:
Since you cite the Basoulis et al paper (ref #46), you will be probably interested in this: https://doi.org/10.5281/zenodo.16922987
On 2020-12-28 18:05:42, user Rogerio Atem wrote:
The 3 preprints of this series on COVID-19 epidemic cycles were <br /> condensed into a single article that summarizes our findings using the <br /> analytical framework we developed. The framework provides cycle pattern <br /> analysis, associated to the prediction of the number of cases, and <br /> calculation of the Rt (Effective Reproduction Number). In addition, it <br /> provides an analysis of the sub-notification impact estimates, a method <br /> for calculating the most likely Incubation Period, and a method for <br /> estimating the actual onset of the epidemic cycles.
We also offer an innovative model for estimating the "inventory" of infective people.
Check it at:
(Revised, not yet copy-edited)<br /> https://doi.org/10.2196/22617
On 2022-08-10 10:36:43, user Leen Jabban wrote:
This paper is now published in Journal of NeuroEngineering and Rehabilitation<br /> https://jneuroengrehab.biom...
On 2020-06-12 07:36:28, user Meki Shehabu wrote:
From my experiences in plant genetics, some QTLs are specific to some populations. can this be true for Africans? might lead to the answer why the covid19 incidence is relatively low in Africa?. I know similar result was reported from China.
On 2025-04-11 20:29:22, user Scott Olesen wrote:
Now published: https://doi.org/10.1016/j.vaccine.2025.127109
On 2022-10-04 16:28:04, user Thomas Arend wrote:
Dear Venkata,
I have some remarks to your study.
The age bands in your tables are very big. We know elderly people were <br /> vaccinated first. Elderly people have a higher risk to die or get <br /> hospitalized from COVID-19.
In the age band > 18 yo this would lead to a pattern as in figure 5b <br /> and 5c. The hospitalization and death rates rise with the start of the <br /> vaccination process in the vaccinated group, just because the members of <br /> the group are older.
After a peak, the rates fall back to a lower level (right side of the figures).
On the other hand the unvaccinated group consists of younger people<br /> as the older people leave the group by vaccination. The rates would <br /> fall with the beginning of the vaccination process and rise later to a <br /> higher level.
This effect can be seen in the ONS data, even when you compare smaller age bands and take the start of the vaccination process into account.
An equivalent argument would be valid for differences in sex.
As I know from Germany, the vaccination rate rises with age. Older people<br /> are more likely to be vaccinated than younger people. Therefore, there <br /> is a bias between vaccinated and unvaccinated people by age and possible<br /> sex.
For risk assessment, you are comparing a group of younger unvaccinated with older vaccinated people. This is misleading. And will probably be the reason for the negative vaccine effectiveness.
The curves in figure 5b and 5c depend highly on the age and sex structure of the groups.
In table 1 you are comparing all ages. The proportion of infected people in the differs largely by age over the time.
In Germany, only ~ 20 % of the age group 60+ yo and 80+ yo got infected <br /> until now. In the age group 5 – 14 yo and 15 – 34 yo, nearly 60 % got infected until now. The incidence varied a lot during the <br /> pandemic. So different time frames differ in the age and sex structure <br /> of the infected people.
Proposals
You should report and discuss the mean and median ages of the groups with standard deviation and IQR.
You should take at least the differences in age and sex into account and<br /> transfer the populations of the groups into a standard population and <br /> calculate the hospitalization and death rates for this standard <br /> population before comparing.
Or:
Even in the age bands 60+ yo the vaccination process produces a bias<br /> by age and sex because the risks rise almost with each age year and female have a lesser risk than male. Therefore, you should <br /> only compare small age bands with a width of five years or less.
You should divide the group of unvaccinated into unvaccinated and still not infected and unvaccinated and at least one time infected. Because infection works similar to vaccination.
Without these improvements, I can't see how you will come to a valid conclusion and result.
Best regards
Thomas Arend
On 2021-02-15 15:10:33, user Paul Wolf wrote:
Near the end of the abstract, you say the data is "suggesting parallel evolution of a trait that may confer an advantage in spread or transmission." Why would this mutation be occuring in different parts of the US and nowhere else in the world? That doesn't suggest convergent evolution, but a common origin.
On 2020-07-06 09:56:53, user Moore wrote:
interesting but you find that there were no events in NSAIDs users not using paracetamol (figure 3) So that presumably all events were in patients using paracetamol (4.1%) or in combined paracetamaol+NASID users. The latter suggests chanelling of NSAIDs to more severe cases resisting to paracetamol, much as was shown for soft tissue infection by S Lesko.<br /> Unfortunately you do not give in figure 3 the number of patients concerned in each group, so that it is not possible for instance to look at poisson estimates (using the upper limit of the 95% confidence interval of 3 for no cases. Of course if all NSAIDs cases were in patients who associated paracetamol to NSAIDs, the conclusion is very different.<br /> Comparing use to non use is really misleading, since is cannot take into account confounding by indication (more severe cases get NSAIDs), and should not be used.<br /> Preferably in these cases where outcomes are associated with symptoms, the safest comparison is users vs user of drugs with the same indication, in this case paracetamol. It would be nice to see separately NSAIDs, paracetamol and NSAIDS+Paracetamol, and neither, and test for interaction.
On 2020-12-30 22:01:49, user Henry wrote:
" translated to a rise of 21.1 nmol/L of 25OHD in the UK Biobank population, a rise that is comparable to what can be achieved with vitamin D supplementation, especially in short courses[38]."
A 21nmol/liter serum raise is not much. That is what you get when you supplement too little vitamin D (400 iu / day for short courses).
Did you compare 150 nmol / L and higer to 30 nmol / L? I would say someone has optimal vitamin d at 150 nmol / L.
On 2020-12-11 16:21:41, user Fred wrote:
Disappointing study. I would not expect that antivirals are of any use if started when patients are already hospitalized. I would recommend to start with antivirals as soon as possible regardless wheter the patient has symptoms or not. But in this case we need studies comprising many more patients than in this small study
On 2022-03-02 17:16:42, user HF wrote:
Is there any SPSS, Strata or R code available to replicate this project?
On 2020-05-15 08:56:29, user Pavel Valerjevich Voronov wrote:
Am I getting it right - AB hardest to infect, but also most deadliest if infected?..
On 2020-03-26 05:18:54, user TreeHugginEnergyWonk wrote:
This is thrilling! People who have already been infected and cleared the coronavirus could donate their blood plasma immune factors to help those suffering more extreme cases of the disease! People could get back to work!
On 2021-11-11 01:06:23, user transplantee wrote:
This paper is now published at https://pubmed.ncbi.nlm.nih...
On 2025-01-02 09:48:12, user Teresa Ramírez García wrote:
Dear Dr. Witt: We have read with great interest your article published in preprint format. In this article, an aspect that we consider confusing is mentioned in relation to our work [1]. We refer to the authors' assertion that “in the FCSRT only 4 words need to be learned in three learning trials, whereas the VLMT requires learning of 15 words in 5 trials” [2].
In this regard, we would like to point out that the FCRST requires you to effectively memorise 16 words spread across 3 trials, not just 4 words, as stated in the original paper by the author who developed the exam. Because it aligns with the original test scales by age and cognitive reserve of the patients in the Spanish population, this test can also be explained in the Neuronorma project, which we use in Spain. Since we discovered a baremation based on the Spanish population [3], this is also the reason why this test is typically utilised in Spain.
1.- Serrano-Castro PJ, Ramírez-García T, Cabezudo-Garcia P, Garcia-Martin G, De La Parra J. Effect of Cenobamate on Cognition in Patients with Drug-Resistant Epilepsy with Focal Onset Seizures: An Exploratory Study. CNS Drugs. 2024 Feb;38(2):141-151. doi: 10.1007/s40263-024-01063-6. Epub 2024 Jan 24. PMID: 38265735; PMCID: PMC10881647.
2.- Witt JA, Badr M, Surges R, von Wrede R, Helmstaedter C. Negative Impact of Cenobamate on Cognition: Dose-Dependent and Independent Effects medRxiv 2024.12.23.24319533; doi: https://doi.org/10.1101/2024.12.23.2431953
3.- Peña-Casanova J, Gramunt-Fombuena N, Quiñones-Ubeda S, et al. Spanish Multicenter Normative Studies (NEURONORMA Project): norms for the Rey-Osterrieth complex figure (copy and memory), and free and cued selective reminding test. Arch Clin Neuropsychol. 2009;24(4):371-393. doi:10.1093/arclin/acp041
Ramirez-Garcia T and Serrano-Castro PJ.
Hospital Regional Universitario de Málaga.
Instituto de Investigacion Biomedica de Málaga (IBIMA-Plataforma Bionand).
On 2020-11-02 00:38:07, user Md Mokhlesur Rahman wrote:
The peer-reviewed version of this paper has been accepted and published online. You can find the paper at the following link.
On 2025-08-01 10:26:25, user reviewer 2 wrote:
It is not clear that some of the findings taken to demand "greater nuance" in interpretation actually do so.
The absolute and relative indicators proposed measure different things. A finding of greater male excess mortality in absolute terms is not in any sense qualified by a finding that this elevation was approximately proportional to baseline differences. Effect measures at different scales are in general different. The combination of the two findings may be a first step into an investigation of the degree of specificity of greater male excess mortality with respect to the pandemic, an issue that can cast light on the causes of differential mortality, rather than on the characterization of the differential itself. Once this is understood, there is no reason to deem the dependence of the absolute excess mortality difference on baseline differences a "limitation". It is also not true that it says "little about whether men are more susceptible to the SARS-CoV-2 virus itself" than women - if by "susceptible" it is meant "higher risk of death". Considering the greater male mortality for a wide array of causes of death, an excess proportional to the baseline excess in fact suggests a similar degree of greater vulnerability.
Variation in excess mortality throughout the course of the pandemic is also hardly a reason to invoke nuance. An endogenous change in the composition of the population at risk, highly expected under very general and simple conditions, can explain this. Mentioning this in a list of plausible factors overlooks the much great prima-facie plausibility of this factor, which is almost bound to have an influence, and clearly predicts excess mortality differentials to be self-limiting. The authors may fruitfully consider, with the aid of a demographic model, what the duration of the period in which greater excess mortality occurred tells us about the underlying difference in the sex-specific distributions of risk. This duration is labeled "short lived" with no clear explanation as to which benchmark is used. It is of course also possible that its duration was shaped by the introduction of vaccination.
Talk of a "universal pattern" is another poor choice of language, in my opinion. No scenario, including a sex difference entirely due to sex-related biological factors, predicts differential mortality to be independent of age and country. Age is a well established demographic risk factor for COVID-19 mortality, and could easily interact with sex-specific biological factors. Countries have, among countless differences, different disease environments - which could also clearly interact with sex-specific biological factors. The discussion of the degree and type of heterogeneity of findings should be substantially more precise - in fact, nuanced, as there is little nuance in moving from the premise that biological factors should predict homogeneous, constant affects whenever they are found.
Some discussion is warranted on the use of all-cause mortality to identify COVID-19 mortality. I did not read previous discussion of this point, but I would expect the proxy to be sound in the initial period of the pandemic, whereas annual fluctuations in the composite of other causes may well become more important in later stages of the pandemic, when COVID-19 mortality is lower. Excess mortality measures in this later period are therefore more uncertain. This should be considered when interpreting findings for those stages - including the cases of relative increase in female excess mortality.
In general, mortality risk differentials are likely related to the overall level of mortality. This may also hold for excess mortality differentials. It seems to me highly likely that greater male excess mortality obtains when excess mortality is higher. At low levels of excess mortality estimates may be less reliable, and mortality selection must be taken into account. The authors should consider exploring this aspect of the problem.
None of the results presented suggests "greater nuance" relative to explanations based on biological factors. Biological factors vary across populations and environments, their distribution varies throughout the course of events that alter the demography of the population. They do not predict overly simple scenarios. Other factors likely play a role, but this role cannot be inferred in such a way.
On 2021-04-25 17:46:32, user Mikko Heikkilä wrote:
The RRs for the Macintyre et al. 2015 and Suess et al. 2012 are also not what they are in the original papers.
For the Cowling et al. 2009 Ollila et al. have used 18 events in an intervention group of 258. The orginal paper has three definitions for an event in the groups: RT-PCR confirmed, Clinical definition 1 (2 symptoms) and Clinical definition 2 (3 symptoms). There were 18 RCT confirmed, 55 Clinical def 1 and 18 Clinical def 2 cases in the intervention group.
On 2020-05-09 15:11:13, user fvtomasch wrote:
Worldwide deficiency started in my opinion back in the late 70's and 80's with the onset of the ozone hole in the southern hemisphere due to the release of CFC'S into the air. Many in Australia were getting skin cancer and if you recall SPF's on sunblocks were 10-15 but were steadily increased to 50-100 and above thus blocking UV rays which are needed to synthesize Vit D. Now 15-20 years later statins were introduced to reduce cholesterol levels and cholesterol is also needed to synthesize Vit D. So even in summer we are not getting the proper amount of D. Perfect storm of disease and death that can be minimized by a 10 cent daily pill.
On 2020-10-29 14:20:08, user Roni Maimon Mor wrote:
You can find the published version of this manuscript at:<br /> https://www.sciencedirect.c...
On 2022-10-29 10:11:21, user samer singh wrote:
Published/peer-reviewed version of the study is available at Clinical and Translational Discovery https://doi.org/10.1002/ctd...
On 2025-11-10 10:25:43, user Karl Morten wrote:
This is intriguing. Are ME/CFS patients better at controlling endogenous viruses/ better at clearing them? It reminds me of Jo Elson's work showing two cohorts of ME/CFS patients have less deleterious mtDNA variants compared to controls https://www.nature.com/articles/s41598-019-39060-1 <br /> Are we looking at some sort of enhanced/different response ? Mildly deleterious mtDNA in healthy controls might better prime an innate immune response ( work of Phillip West and others)
On 2024-05-01 03:00:07, user Bernadette wrote:
Firstly my thanks to each one of you. This paper gives me hope that diagnostic and treatment guidelines for ‘TSW’ can be developed. I have a H/O of 70+ years of skin problems. Having struggled for 4 years with skin rashes which present to me, and to a Sydney based GP with an interest in TSW, as being consistent with TSW, I have experienced the frustration of presenting to dermatologists who say TSW is not an ‘accepted’ skin condition even though I have experienced and photographed my red sleeve, elephant skin on my ankles and wrists, non stop oozing on my face, neck and ears, non stop skin flaking, hair loss, heat, pain and intense itching. I have experienced the isolation and depression too often associated with this condition. Recently I have focused on managing heavy staph and fungal concentrations and have seen significant improvements. So I’m hoping this paper will act as a catalyst for a comprehensive focus on TSW so that we, those affected, can access medical expertise without running the gauntlet of being dismissed and belittled. Again. Thank you.
On 2021-09-16 08:25:05, user Dr Siona Bastable wrote:
Overlap with effects of ADHD symptomology is striking....
On 2021-10-31 15:46:14, user Jan Brauner wrote:
This manuscript has been published now: https://www.nature.com/arti...
On 2025-09-30 03:53:09, user Eero Raittio wrote:
Published in Community Dentistry and Oral Epidemiology <br /> https://onlinelibrary.wiley.com/doi/10.1111/cdoe.70026
On 2020-05-30 11:48:52, user LizJMD wrote:
It would be of interest to know more of the baseline characteristics of the patients. I wonder if sHLH is related to baseline characteristics like younger age (viz the speculation on relation to pediatric multisystem inflammatory syndrome), obesiity, or other known risk factors for poor outcome.
A letter in JAMA detailed autopsy findings in 10 German patients (Schaller et al), ages 64 to 90, in which no vascular thrombosis was seen, just "pure" histologic findings of ARDS. "Only" 2/10 had morbid obesity.
I look forward to more revelations from the remaining subjects in your 67 patient cohort (seems to me only 25 analyzed) including distribution of ACE 2 receptor with respect to pathology and SARS CoV 2 infection.
Liz Jenny MD Jacobi Medical Center, Bronx NY
On 2024-05-10 18:06:25, user Adam Jones wrote:
Now published in Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2024.108545
On 2020-04-23 21:28:37, user David Scott wrote:
This is interesting, using text mining to estimate how many people are sick. Perhaps people may be more inclined to post that they are sick than actually go to the doctor? I think the article is worth reading.
On 2021-02-18 08:57:15, user Meri Tulic wrote:
Accepted for publication in Journal of Investigative Dermatology (February 2021)
On 2020-09-04 18:13:35, user mzbaz wrote:
Thanks for your kind words and great suggestions. We had lumped the effects of air filtration and recirculation into the deactivation/removal rate of the virus (lambda_v), leaving it to the user to calculate separately. Now, in the revised spreadsheet, available at http://web.mit.edu/bazant/w..., we allow the user to enter the primary outdoor air fraction and the filtration efficiency of any filters on the return air (HEPA, MERV,...). -Martin
On 2020-04-02 13:15:38, user Sinai Immunol Review Project wrote:
COVID-19 infection induces readily detectable morphological and inflammation-related phenotypic changes in peripheral blood monocytes, the severity of which correlate with patient outcome<br /> doi: https://doi.org/10.1101/202...
SUMMARY: This study is based on flow cytometry immunophenotyping of PBMCs from 28 patients diagnosed positive for SARS-Cov2 (COVID19). The authors identify a population of abnormally large (FSC-hi) monocytes, present in COVID19 patients, but absent in PBMCs of healthy volunteers (n=16) or patients with different infections (AIDS, malaria, TB). This FSC-hi monocytic population contains classical, intermediate and non-classical (monocytes (based on CD14 and CD16 expression) that produce inflammatory cytokines (IL-6, TNF and IL-10). The authors suggest an association of FSC-hi monocytes with poor outcome and correlate a high percentage of FSC-low monocytes, or higher ratio of FSC-low/hi monocytes, with faster hospital discharge.
LIMITATIONS: While identification of the monocytic population based on FSC is rather robust, the characterization of these cells remains weak. A comprehensive comparison of FSC-hi monocytes with FSC-low monocytes from patients and healthy controls would be of high value. It is unclear if percentages in blood are among CD45+ cells. Furthermore, it would have been important to include absolute numbers of different monocytic populations (in table 1 there are not enough samples and it is unclear what the authors show).<br /> The authors show expression of the ACE2 receptor on the surface of the monocytes, and highlight these cells as potential targets of SARS-Cov2. However, appropriate controls are needed. CD16 has high affinity to rabbit IgG and it is unclear whether the authors considered unspecific binding of rabbit anti-ACE2 to Fc receptors. Gene expression of ACE-2 on monocytes needs to be assessed. Furthermore, it would be important to confirm infection of monocytes by presence of viral proteins or viral particles by microscopy.<br /> Considering the predictive role of FSC-hi monocytes on the development of the disease and its severity, some data expected at this level are neither present nor addressed. Although the cohort is small, it does include 3 ICU patients. What about their ratio of FSC-low vs FSC-hi monocytes in comparison to other patients? Was this apparent early in the disease course? Does this population of FSC-hi monocytes differ between ICU patients and others in terms of frequency, phenotype or cytokine secretion? <br /> In general, figures need to revised to make the data clear. For example, in Fig. 5, according to the legend it seems that patients with FSC-high monocytes are discharged faster from the hospital. However according to description in the text, patients were grouped in high or low levels of FSC-low monocytes.
RELEVANCE : Despite the limitations of this study, the discovery of a FSC-high monocyte population in COVID-19 patients is of great interest. With similar implication, a the recent study by Zhou et al (https://www.biorxiv.org/con... "https://www.biorxiv.org/content/10.1101/2020.02.12.945576v1)") identified a connection between an inflammatory CD14+CD16+ monocyte population and pulmonary immunopathology leading to deleterious clinical manifestations and even acute mortality after SARS-CoV-2 infections. Although the presence of these monocytes in the lungs has yet to be demonstrated, such results support the importance of monocytes in the critical inflammation observed in some COVID19 patients.
Review as part of a project by students, postdoctoral fellows and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai
On 2020-08-17 15:27:01, user Mike Stevens wrote:
Wow! If this drug is so "toxic" taken in a dose just above what is normally indicated, then it should not be on the shelves or prescribed.<br /> Such a low/narrow therapeutic index is untenable in clinical medicine.
On 2020-05-25 10:09:40, user Rai Zure wrote:
I certainly found Peters comment/question under the paper it was intended for.
It is indeed puzzling that the prevalence could be so high in Milan even before the outbreak and nobody noticed. Known hotspots in Austria or Czech republic had under 5% prevalence -after the outbreak.
On 2022-02-01 17:13:38, user Ilya Gordeychuk wrote:
A disclaimer. I'm employed at the Chumakov Center, the developer and manufacturer of CoviVac.
First of all, thank you for your work. Clinical description and assessment of cases of symptomatic SARS-CoV-2 infection in vaccinated people during the circulation of emerging virus variants are essential both for the general public and for the healthcare system.
Still, I think some interpretations require further clarification. The first two questions coming in mind after reading the paper are:
Did you do any genome sequencing of the virus isolates during your work? It looks now that you assume that those cases were all caused by the delta variant based only on the general epidemiological data saying that delta was predominant in St. Petersburg at the period of observation. If so, the title of the study may be a bit misleading, as it states that those cases were all delta.
You state throughout the manuscript that you see significant differences between the effectiveness of the three vaccines, but these interpretations are not supported by the data. <br /> Namely, you state in the abstract that "In contrast to other Russian vaccines, Gam-COVID-Vac is effective against symptomatic SARS-CoV-2 infection caused by Delta VOC", on Page 8 that "CoviVac usefulness is also doubtful" etc. At the same time there is no data in the paper supporting this statement. There is no statistical comparison between the CoviVac group and other vaccine groups. Moreover, I performed a statistical assessment of the data presented in Table A1 and there is no statistically significant difference between the CoviVac group and the Gam-COVID-Vac group, so the data presented in this table directly contradicts your interpretation of the data.
Best regards,<br /> Ilya Gordeychuk
On 2023-04-21 12:49:03, user antonia peros wrote:
I believe that the topic of your research is very important and current, however, I have several methodological objections. <br /> Although the authors pointed out the limitations, they made quite strong conclusions and recommendations despite too small, non-randomized sample and a cross-sectional design without a control group.<br /> The use of the used instruments is very questionable when it comes to recalling satisfaction, self-esteem, and reduction in depression from 6 months ago. I also think that the fact that the respondents were familiar with the purpose of the research could have contributed to the recall bias.<br /> An important factor in your research could be how long the subjects exercised, and you did not collect that data. What their target is in the research is also vaguely defined. I recommend including some more objective criteria for that.
On 2025-01-13 17:21:13, user Tara wrote:
Now published: https://doi.org/10.2807/1560-7917.es.2024.29.38.2400592
On 2020-04-26 00:39:15, user tsuyomiyakawa wrote:
There are two major issues that make the design of this study inappropriate for examining the BCG hypothesis.
The efficacy of the BCG is supposed to wane over time and so the most of the protective effects of BCG in aged people, is any, is supposed to be mediated largely by herd immunity. Herd immunity would occlude the discontinuity.
BCG is a weakened version of tuberculosis (TB) and TB infection would exert equivalent or even stronger protective effects with the BCG hypothesis. Before implementation of BCG policy, most of the countries were high tuberculosis burden countries. So aged people in those countries are expected to be protected by their experience of TB infection in a similar way BCG protects, under the BCG hypothesis. Note that Vietnam and Thailand are still high TB burden countries.
Also, there are a few minor issues that I'd like to point out.
In Czech, it is interesting that there are some children under 10 years old who were tested positive and are not covered by BCG. In other countries, few children, who are covered by BCG, were tested positive.
In Figure 1 or in Supp. Figures, similar panels for the other analyzed countries should be also shown.
The raw data on which Fig 2 should be made available. Apparent positive correlation between BCG coverage and "the log cases per thousands" is interesting but it is likely to be a spurious correlation. Trying to identify the factor underlying such correlation would be important.
On 2021-09-12 15:29:25, user Guy André Pelouze wrote:
May I raise a question: was an Anti Sars antibody test done before vaccination in all the young people of this study?<br /> I didn't find the answer in the paper. <br /> if not it could be a bias as we know that one shot is enough in patients that had a previous infection.
On 2020-04-17 13:01:47, user Greg Blonder wrote:
Two comments:
1) Can you also test whether 7 days in say modest heat (120F) is effective? This would allow people to use a buffer stock protocol- in on Monday, out the next week, and would decontaminate PPE in general. Absolutely critical in preventing death in developing nations. Even if it does not kill all associated microbes, like MERS, any improvement is needed.
https://1drv.ms/b/s!AkTaUk4...
2) Can you please influence 3M to could create a heat decontamination grade mask, perhaps with a color changing use indicator for tracking number of cycles? Heat decontamination is faster, cheaper and has improved coverage compared to peroxide vapor .
On 2020-08-10 08:17:44, user OxImmuno Literature Initiative wrote:
On 2021-05-27 09:29:55, user MarcWathelet wrote:
What a joke, after your "mistake" inverting the control and IVM arm of the Niaee study, the RR goes from 1.11 to 0.37 yet you dare to not change a single word in your conclusion:<br /> In comparison to SOC or placebo, IVM did not reduce all-cause mortality,<br /> length of stay or viral clearance in RCTs in COVID-19 patients with mostly mild disease.<br /> IVM did not have effect on AEs or SAEs. IVM is not a viable option to treat COVID-19<br /> patients.<br /> The new "scientists": we don't care about data contradicting our preconceived interpretation, we stick to our guns! Propaganda Abteilung!
On 2021-05-29 20:55:44, user Robert Clark wrote:
Seriously, it’s like some researchers opposed to the concept of EARLY treatment of COVID will go to any lengths to provide evidence against it, even if it crosses the line of scientific ethics.
Sorry, to have to say this but the authors no longer have any credibility on this issue.
Extremely important to recognize the importance of this: to provide evidence against IVM researchers have had to change data to fit their conclusion.
What does that tell you about the effectiveness of IVM?
Robert Clark
On 2021-12-30 12:21:53, user Raster wrote:
It was not ignored, it simply wasn't confirmed by evidence.
On 2021-12-24 21:24:16, user Shannon Rowland wrote:
How much shorter was the duration in the vaxed group vs unvaxxed? And those who took something other than Moderna- can I assume that they didn’t have the same benefit?
On 2020-05-27 16:52:06, user Gordon Freeman wrote:
Can you please share the source code....
On 2020-08-24 17:50:51, user Puvvada Rahul krishna wrote:
We would like to withdraw the article from MedRxiv. The reason for withdrawal was plagiarism issue while publishing to other journal's
On 2020-05-24 21:33:11, user Tim Tarr wrote:
DOXY was suggested as replacement for azithromycin for those with heart issues. Seems azithromycin may compound HCQ risk to the heart. Now put Zinc in the mixture.<br /> DOXY+HCQ+Zinc sulfate <br /> The lab work should be run for vitamins D&C deficiency and Zinc. Lab work on kidney & liver status is pretty standard for admission.Also if heart function not known that should be checked, also usually a basic admission process.
On 2020-04-22 19:08:57, user Brian_Dell wrote:
This is analogous to the "sputum" tests widely used in Germany, right?
On 2020-06-24 11:34:40, user Renzo Huber wrote:
This is a nice review that might also be valuable to the field of layer-fMRI. <br /> I think the manuscript might benefit from an additional brief discussion of the related laminar connectivity findings from non-invasive human fMRI studies:
-> layer-dependent connectivity in Fig. 6 and 7 of the following study: <br /> Huber L, Handwerker DA, Jangraw DC, et al. High-Resolution CBV-fMRI Allows Mapping of Laminar Activity and Connectivity of Cortical Input and Output in Human M1. Neuron. 2017;96(6):1253-1263.e7. doi:10.1016/j.neuron.2017.11.005
-> layer-dependent connectivity with gppi in this study: <br /> Sharoh D, Mourik T van, Bains LJ, et al. Laminar Specific fMRI Reveals Directed Interactions in Distributed Networks During Language Processing. PNAS. 2019:1907858116. doi:10.1101/585844
-> layer-dependent hierarchical connectivity discussed in this study: <br /> 1. Huber L, Finn ES, Chai Y, et al. Layer-dependent functional connectivity methods. Prog Neurobiol. 2020:in print. doi:j.pneurobio.2020.101835
On 2020-08-21 04:26:05, user N M C wrote:
Very odd FDA put this on hold,
On 2020-04-21 22:06:22, user Senad Hasanagic wrote:
Totally flowed retrospective analysis because baseline Clinical characteristics are missing ( not reported) in significant number of patients. So adjustments not possible.
On 2020-05-04 11:11:16, user Will Jones wrote:
What was the median number of days between admission and death?
On 2021-08-29 20:54:09, user peter_wark wrote:
Thanks again Recovery trial.<br /> Participants admitted with COVID19; unable to maintain SpO2 <94% despite FiO2 0.4.<br /> Mean age 57yrs<br /> Primary outcome was intubation or mortality at d30.<br /> CPAP HR 0.72 (0.53-0.96) p=0.03<br /> HFO2 0.97 (0.73-1.23) p=0.85<br /> The number needed to treat for CPAP was 12 (95% CI, 7 to 105) and for HFNO was 151 (95% CI, number needed to treat 13 to number needed to harm 16).
On 2020-04-25 14:15:03, user Wouter wrote:
End of the week. Agreed ;-)
On 2020-05-13 17:00:14, user Sinai Immunol Review Project wrote:
Main Findings<br /> The immunity of the mucosa between the mother and the newborn against COVID-19 was tested. The secretory antibody -IgA of breastfeeding milk, shown immune response to the Receptor Binding Domain (RBD) of SARS-CoV-2 Spike protein.
Limitations<br /> Further studies are needed to understand the types of vaccines and routes of administrations in terms of protective antibodies against SARS-CoV-2 in human milk.
Significance<br /> Immune-modulating factors in breast milk may exert a significant impact on the infant’s developing immune system preventing or mitigating SARS-Cov-2 infection.
Reviewed Martinez-Delgado Gustavo as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.
On 2022-02-04 14:45:12, user Mukhtar wrote:
Hey, we have three affected individuals and found a very convincing homozygous missense mutation in KCNC2. The variant co-segregates with disease phenotype. Parents are heterozygous carriers. The phenotype of patients is vision impairment. I am just wondering if your patient also has some vision defects.
On 2022-02-09 20:37:04, user anon wrote:
This is completely nonsensical after a population wide study by Patone et al. showed the pooled risk of developing myocarditis to be higher after any mrna vaccine series compared to covid infection for males <40. Yet 1000x higher from covid? I have to say, even among covid long hauler communities, compared to communities with vaccine side effects, the prevalence is much higher in the vaccine communities. Where am I missing the extra 1000 people for every extra person in the vaccine communities?
On 2021-11-07 19:23:55, user Eleutherodactylus Sciagraphus wrote:
It is relevant to note that this preprint (along with other two from the same group) includes data from human subjects that are under ethical scrutiny. The majority of patients enrolled were not informed nor agreed on participating in the study. The Brazilian National Comission for Research Ethics (CONEP) has been bypassed and is now investigating this case.
https://brazilian.report/li...<br /> https://www.emergency-live....<br /> https://www.dire.it/14-10-2...<br /> https://www.matinaljornalis...<br /> https://g1.globo.com/rs/rio...
On 2020-09-18 14:18:31, user mark.goldberg@mcgill.ca wrote:
Similar to the ecological studies on mortality fro covid and air pollution this study is biased. You may want to read the paper by Paul Villeneuve and myself in Environmental Health Perspectives: "Methodological Considerations for Epidemiological Studies of Air Pollution and the SARS and COVID-19 Coronavirus Outbreaks", https://doi.org/10.1289/EHP...
On 2021-12-16 11:48:59, user Timm wrote:
Congratulations to this important study! Why do you think the data hints towards affinity maturation and not just higher titers?
On 2021-03-24 21:24:49, user Erik von Elm wrote:
Could the authors please clarify whether the study has been commissioned by a corporate client (as suggested at https://idalab.de/success-s... "https://idalab.de/success-stories/improving-rd-productivity/)") or not? In the manuscript they state that "no funding bodies had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."
On 2022-06-02 18:37:12, user Nils Yang wrote:
This paper was accepted at Lancet Child & Adolescent Health.
On 2020-04-12 23:44:57, user Xavier de Roquemaurel wrote:
Please re-run the analysis strain by strain. Regrouping all countries using Tokyo172 together, all Copenhagen 1331 together, all BCG Russia together and all the other strains together. You may achieve an extreme correlation with Tokyo172.
Then, if you can, run all possible other factors: social distancing, masks usage, confinement, average % of population in overweight, blood group predominance in population, ethnicity (for enzyma differences not for skin colours), % of population smoking, level of air pollution... or other factors that may come to your mind.
Let’s get the BCG hypothesis fully checked and then let’s act according to the results. Thanks!
On 2021-06-07 07:55:12, user David Curtis wrote:
The confounding effects of ancestry on genetic risk prediction are well established. See for example:
Polygenic risk score for schizophrenia is more strongly associated with ancestry than with schizophrenia. <br /> https://journals.lww.com/ps...
On 2021-11-20 23:32:38, user Gordon V. Cormack wrote:
Were the previously infected also vaccinated, either before or after their infection?
Edit: I think I answered my own question: <br /> When we examined HCWs (n=423) with infections occurring before vaccination, no re-infection was observed, accumulating 74,557 re-infection-free person-days (starting 10 days after initial infection and censoring at the date of receiving their first vaccine dose). Further, after vaccination, previously infected HCWs did not contribute any breakthrough infection events among the vaccinated HCWs.
On 2020-04-11 08:07:44, user Jeff Aronson wrote:
This looks like an interesting study, with an important warning about the combination of hydroxychloroquine + azithromycin, which people are beginning to use. But why misleadingly title the paper "Safety of hydroxychloroquine …" when what is being reported is serious adverse events, i.e. unsafety? I hope that when the authors prepare their paper for peer review, they will use a more accurate description.
On 2025-10-06 18:12:00, user David Maxen wrote:
I am confused as to why k=1,...,13 in equation 1. I accept that the transition probabilities T(A->A)+T(A->T)+T(A->C)+T(A->G)=1, and likewise for T, C and G. This explains 4 of the 13 values of k, but not the others. I am also confused as to what is meant when k=13; in this case, s ranges from 49 to 52. But there are only 16 dinucleotides, T^52 t->t+1 doesn't make sense either. Otherwise I am very interested in this paper, so I am very interested to hear your response. Best wishes, David
On 2020-09-30 18:53:22, user James Rubin wrote:
Please note the authors have identified an issue with the underlying dataset that was analysed for this pre-print. Specifically, around 3% of respondents in the dataset reported having been contacted by NHS contact tracers and asked to quarantine. According to official data from NHS Test and Trace, this should be less than 1%. Given this difference, it is likely that we will revise our interpretation of the quarantine data in the final peer-reviewed paper. Until then, as noted in the manuscript, the data relating to quarantine should be treated with caution.
On 2022-01-29 14:34:02, user Alberto wrote:
Thank you for highlighting this problem. It's amazing that indoor air quality has been so ignored apart from some "open the windows" advise.
On 2020-02-21 07:57:05, user hym4063 wrote:
Good study. No new case is expected from mid March. In other words, it is still very DANGEROUS now!!
On 2020-05-01 15:58:00, user Diego Fleitas wrote:
First, thank you for your research.<br /> Second, I have some doubts about your weighting criteria. Because of: it is not known the relation between contagion and socio demographic aspects; weighted results are almost three times higher than raw prevalence what looks out of scale; and also the relation of 80 times fold between actual diagnosed and projected looks a bit out of scale.<br /> Best
On 2021-08-11 15:21:36, user circleofmamas wrote:
They need to include vaccination status of the cases, hospitalizations and deaths. Canada is one of the most widely vaccinated countries in the world, and we can see from Israel data and UK data that the vaccinated are particularly vulnerable to the Delta variant.
On 2021-04-19 18:28:52, user lbaustin wrote:
Has this been submitted (or accepted) in a journal yet?
On 2020-02-15 18:42:07, user Giuseppe Lapadula wrote:
How was determined the index date for calculating the incubation period? How can it be 0 in some patients?
On 2020-03-03 14:31:32, user palazmarkazi wrote:
So if the assumption is correct, then we should see those who suffer from it decline the following week
On 2020-08-25 09:01:02, user Tjabbe wrote:
Evidence for what, that it doesn't work for late stage covid in hospitalised patients? Is that even news? How come at this stage in the pandemic we are still publishing reports that claim medication be ineffective "for treating covid19" when in fact it was only tested for patients with severe covid19 already in the hospital. We all know patients will not be sent to a hospital in the Netherlands for covid unless they have progressed pretty far. <br /> The report describes hcq being used on patients when deteriorating in several of the hospitals, affecting mortality, and media outlets conveniently leave out this part of the puzzle.
If you want to curb covid, or if you want to write off medication as being useless "for covid" , start doing trials on early outpatient treatment.
On 2020-11-16 08:49:06, user Mike Maglothin wrote:
I've seen several studies attributing all excess deaths to CoVid. I agree... BUT.. what they show in their modeling is a correlation to CoVid. The excess deaths could very easily be from delayed procedures or people being unwilling/unable to get a procedure done in a timely fashion. I know many procedures were delayed, especially during the beginning of the pandemic when hospitals were being "reserved" for CoVid. Would be interesting to see the Excess death correlation only after August.
On 2020-04-08 00:15:34, user Sinai Immunol Review Project wrote:
Clinical features and the maternal and neonatal outcomes of pregnant women with coronavirus disease 2019
Keywords
Pregnancy, SARS-CoV2, neonatal and maternal Covid-19 outcome
Key findings
33 pregnant woman and 28 newborns were included in this retrospective multi-center study, conducted at 5 hospitals in Wuhan and Hubei province, China, between January 1 and February 20, 2020. All women were diagnosed with Covid-19 by qPCR or viral gene sequencing based on the Chinese New Corona Pneumonia Prevention and Control Program, 6th edition, and were further subdivided into four groups based on clinical severity: (1) mild, presence of mild clinical symptoms without radiological abnormalities; (2) moderate, fever or upper respiratory symptoms as well as radiological signs of pneumonia; (3) severe, at least one of the following: shortness of breath/respiratory rate >30/min, resting oxygen saturation SaO2<93%, Horowitz index paO2/FiO2 < 300 mmHg (indicating moderate pulmonary damage); and (4) severe-acute, acute respiratory distress with need for mechanical ventilation; systemic shock; multi-organ failure and transfer to ICU. Maternal admission to ICU, mechanical ventilation or death were defined as primary outcomes; secondary study outcomes comprised clinical Covid-19 severity in both mothers and newborns, including development of ARDS, neonatal ICU admission as well as mortality.
Maternal characteristics and outcome: 3 out of 33 women were in their second trimester of pregnancy (17, 20 and 26 weeks), and 15/33 (45.5%) had a previous history of underlying chronic health disorders including cardiovascular, cerebrovascular or nervous system disease. Common Covid-19 symptoms at presentation were fever (63.6%), dry cough (39.4%), fatigue (21.2%), and shortness of breath (21.2%). Less common symptoms included diarrhea, post-partum fever, muscle ache, sore throat and chest pain. 4 (12.1%) pregnant women had no apparent symptoms. The majority of cases were classified as mild (39.4%) or moderate (57.6%); however, one woman developed severe Covid-19. 40.6% of women were diagnosed with bilateral pneumonia, 43.8% presented with unilateral pneumonia, and 15.6% showed radiological ground-glass opacity. 87.9% of women required oxygen administration, and one (3%) woman had to be put on non-invasive mechanical ventilation (primary outcome). 81.5% of women had a C-section and only 5% had vaginal deliveries. Obstetrical complications were seen in 22.2% of women, including three cases of preterm rupture of membranes, two cases of hypertensive disorders of pregnancy, and one case of spontaneous preterm labor. Five pregnancies were ongoing at the end of the observation point of this study; one woman decided to have her pregnancy terminated. Neonatal outcome: Out of 28 newborns included in this study, 35.7% were born preterm at <37 weeks of gestation with Apgar scores ranging from 8-10/10 at 1 min and from 9-10/10 after 5 min, indicating normal heart and respiratory rates. 17.9% of newborns were of low birth weight (not specified) and 14.3% showed signs of fetal distress (also not specified). According to the authors of this study, none of the newborns presented with clinical Covid-19 symptoms. However, one newborn, delivered at 34 weeks of gestation, was diagnosed with (apparently Covid-19 unrelated?) ARDS and transferred to NICU (secondary outcome). Of 26 newborns tested for SARS-CoV2, only one was found positive and showed radiological signs of pneumonia, but no clinical symptoms of Covid-19. It remains unclear whether this was the same case as the newborn diagnosed with ARDS. The affected newborn did not require any treatment and was discharged at 16 days post birth. In summary, the primary outcome “mechanical ventilation” in pregnant women was rare (3%), no other primary outcomes were reached. Most Covid-19 cases in pregnant women were described as mild to moderate. Only one of 28 (3.57%) newborns was diagnosed with ARDS (secondary outcome).
Potential limitations
Major limitations of this study are its small size and the rudimentary and at times inadequate description of patient specifics. For example, underlying health conditions that might be affecting Covid-19 outcome in pregnant women should have been clearly specified (other than being of be listed (not just <37 weeks). Given that maternal infection status seemed mostly unknown at the time of birth and, more importantly, that the majority of cases in this study were clinically asymptomatic or mild to moderate, it remains unclear whether the C-sections performed were a medical necessity or elective procedures. This is of importance and should have been discussed. With regard to neonatal outcome, it is also not apparent whether the newborn found to be infected with SARS-CoV2 and the case diagnosed with ARDS were the same individual. If this was the case, it would be incorrect to refer to all newborns as asymptomatic. Additionally, it seems somewhat unlikely that a newborn with a near-perfect Apgar score would present with ARDS immediately after birth. Likewise, any individual diagnosed with ARDS would certainly be expected to receive supportive treatment including (invasive) mechanical ventilation. While it is highly relevant that overall clinical outcome in pregnant women diagnosed with Covid-19 seems better than in SARS or MERS (as discussed by the authors), it nevertheless needs to be stressed that more than 37% of newborns in this study were delivered preterm and that the obstetric complication rate of 22% seems higher than non-Covid-19 average.
Overall relevance for the field
Observations in this study confirm some of the findings published in a case series by Yu N et al. (Lancet Infect Dis 2020; https://doi.org/10.1016/ S1473-3099(20)30176-6). However, due to the relatively small study size of 33 pregnant women and 28 newborns, this study lacks statistical power and final conclusions on Covid-19 outcomes in pregnant women and newborns cannot be drawn. Yet, the data collected here are important and should be incorporated into larger data sets for more insight. Understanding the clinical course and effects of Covid19 in both pregnant women and newborns is essential, and while there are some recent publications on vertical SARS-CoV2 transmission between mothers and newborns (Dong L et al, JAMA March 26, 2020, doi:10.1001/jama.2020.4621; Zeng H et al, JAMA March 26, 2020, doi:10.1001/jama.2020.4861) as well as on neonatal infection at birth (Zeng L et al, JAMA March 26, 2020, doi:10.1001/jamapediatrics.2020.0878), our knowledge of how these patient subsets are affected is still very limited.
This review was undertaken as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.
On 2020-06-12 15:57:05, user Rosemary TATE wrote:
What is the correlation hypothesis? With all due respect I think that a course in statistics could be very beneficial for the author.
On 2021-05-26 02:24:19, user ????? ????? wrote:
Hi, I'm Dr.Niaee and I was surprised that even basic data from our RCT is completely mispresented and is WRONG. We had 60 indivisuals in control groups and 120 in intervention groups and even this simple thing is mispresrntated.