On 2021-03-10 12:58:49, user Zed wrote:

So it's not working in early treatment, and during hospitalisation (cf: https://www.clinicalmicrobi... "https://www.clinicalmicrobiologyandinfection.com/article/S1198-743X(20)30505-X/fulltext)")

On 2021-03-11 21:24:49, user disqus_foVd2sEK3I wrote:

Thank you for this important work. I was hoping to take a closer look at the model, only to find out that it was not included. It would be useful to people like me to include the new model's equations for reproducibility.

On 2021-03-12 16:18:56, user NickArrizza wrote:

Are you aware that up to 80% of the co-morbid conditions associated with<br /> 94% of all deaths from COVID-19 are totally preventable (and reversible<br /> within weeks) with a whole plant based diet that lowers inflammatory <br /> markers and hypercoagulability thought to be highly correlated with <br /> severity of illness in COVID-19?

On 2021-03-13 07:53:06, user Motaz wrote:

Thank you for such a great work. I just want to point out in the list of figures at the end, "Fugure 4" is misspelled.

On 2021-03-15 17:04:19, user Eli Yazigi wrote:

Decoding Distinctive Features of Plasma Extracellular Vesicles in Amyotrophic Lateral Sclerosis

Key main ideas in the paper:<br /> • Nickel-Based Isolation (NBI) of extracellular vesicles (EVs) is an effective technique that both preserves the integrity of EVs and easy carry out in a clinical setting.<br /> • Extracellular vesicles in Amyotrophic Lateral Sclerosis (ALS) have distinctive features—in terms of size distribution and protein composition—that are different from EVs of patient with other muscular degenerative diseases (MD).<br /> • The amount of accumulated TDP-43 is indicative of the pace of progression of ALS. Increased accumulation of TDP-43 indicates faster progression of ALS in patients.

Main contribution to the field: The paper established that size distribution and composition of plasma extracellular vesicle can be reliably used to distinguish ALS from other muscular degenerative diseases.

On the scale of 5 (breakthrough) to 1 (no contribution to the field) I would rate the contribution of this paper at 4. The paper provides fast, reliable, and easy technique for isolation of EVs in clinical settings. Using this technique to analyze composition and size of EVs helps in making differential diagnosis.

The conclusions the authors draw in this paper follow experiments performed. And the assumptions made by authors are reasonable and well-thought. However, I think expanding the age range for participants to include younger patients would enhance the credibility of the data and provide for crucial insights.

One disadvantage of using NBI, is that it does not allow for isolation and distinction of extracellular vesicles that are generated through different biological processes (i.e., exosomes vs. microvesicles). These different types of vesicles are regulated in different manner and contain different cellular components.

On a scale of 5 (great) to 1(muddled), I would rate the writing in the paper at 4. There are few typos and grammatical errors. But for the most part the writing was clear and concise. I had to re-read the discussion section couple of times to understand to various conclusions and connect them together. Overall, algorithms are clearly explained in the paper. The logical follow in the paper is smooth and relatively easy to follow. Nonetheless, I think that the connection among various conclusions in the paper could better emphasized.

I think this paper will have a profound, lasting impact in clinical settings. It outlines the use a creative method to draw differential diagnosis among ALS and other MD diseases. The reliability and ease of method presented in the paper along with the data will prove to be revolutionary in the field of medicine.

On 2021-03-16 00:48:29, user Brian wrote:

The main conclusion is driven by a particular 14 day past 2nd dose counterfactual which does not seem realistic in the context of other data. These are the are the graphs in the supplementary material. It makes VE look higher than it likely is during that timeframe. Otherwise results inline with other papers.

On 2021-03-16 18:23:19, user Zhe Zheng wrote:

It's a really interesting finding! Will be nice to know what kinds of NPI have been implemented in Lyon. This will enable a comparison between different countries.

On 2021-03-18 06:40:29, user CD wrote:

I have not read the full paper. Cautious comment: Recruitment between July and December is too large an interval. For example, if in one region most of the recruitment was done in July and in another most aas in December, this will affect the results.

On 2021-03-19 14:03:00, user Wendy Verelst wrote:

Aren't people living with -12y just younger than people without -12y old? -> less risk of hospitalisation .

On 2021-03-21 03:04:06, user Rick Shalvoy wrote:

Very encouraging data. This appears to be the textbook definition of a successful screening tool. Now that the U.S. FDA has finally released a template for device developers to use for EUA submissions when the developer is seeking to obtain a screening authorization, FDA authorization for OTC use of any properly validated device that screens for olfactory dysfunction should, and hopefully will, be granted relative soon after submission.

On 2021-03-24 00:09:50, user Elle wrote:

Also, how is it that patients were not broken out into smokers/non-smokers? All of these symptoms I think would be exacerbated by a smoking habit.

On 2021-03-24 15:21:57, user evacguy wrote:

I am pleased to annouce that this paper was accepted by the Journal of Travel Medicine following peer review on 11/02/21. It is noted that none of the findings, results or conclusions from the first draft have changed. The authors thank the reviewers for their insightful comments and suggested changes which improved our paper. The peer reviewed paper can be freely downloaded using the following link: https://doi.org/10.1093/jtm...

On 2021-04-05 17:44:22, user Armchair Hydrogeologist wrote:

Graph labels are swapped in Figure 1. Should be PepcidAC + Aspirin is 50% not other way around.

On 2021-04-06 17:16:29, user Rick Clem wrote:

I was infected in December along with my whole family. Loss of smell and<br /> a little lethargy was all we experienced. I have wondered if our luck <br /> was attributed to low loading factor or other. So I wonder on the <br /> degree of anitbody presence I attained from the infection. I received <br /> my 1st Moderna shot three weeks ago. Hit me like a freight train after <br /> 10 hours. Extreme fatigue, some headache. My thought is now directed <br /> to skipping my 2nd shot. Reading in the current studies on the <br /> necessity of a second shot, I hope they consider intensity of the <br /> previous infection in their studies. It would help folks like me to <br /> make a more informed decision on whether or not to ignore Fauci and the <br /> CDC's generalisms on needing a second shot.

On 2021-04-12 13:32:42, user H Arnold wrote:

Fantastic paper! What makes me a bit wonder is the discordance to the publications by Yost et al 2019 and Wu et al. 2020. Both report the replacement of T cells in the tumor (different entities) from external sources upon successful ICI.

Yost KE, Satpathy AT, Wells DK, Qi Y, Wang C, Kageyama R, McNamara KL, Granja JM, Sarin KY, Brown RA, Gupta RK, Curtis C, Bucktrout SL, Davis MM, Chang ALS, Chang HY. Clonal replacement of tumor-specific T cells following PD-1 blockade. Nat Med. 2019 Aug;25(8):1251-1259. doi: 10.1038/s41591-019-0522-3. Epub 2019 Jul 29. PMID: 31359002; PMCID: PMC6689255.

Wu TD, Madireddi S, de Almeida PE, Banchereau R, Chen YJ, Chitre AS, Chiang EY, Iftikhar H, O'Gorman WE, Au-Yeung A, Takahashi C, Goldstein LD, Poon C, Keerthivasan S, de Almeida Nagata DE, Du X, Lee HM, Banta KL, Mariathasan S, Das Thakur M, Huseni MA, Ballinger M, Estay I, Caplazi P, Modrusan Z, Delamarre L, Mellman I, Bourgon R, Grogan JL. Peripheral T cell expansion predicts tumour infiltration and clinical response. Nature. 2020 Mar;579(7798):274-278. doi: 10.1038/s41586-020-2056-8. Epub 2020 Feb 26. PMID: 32103181.

On 2021-04-12 15:56:55, user Philip Machanick wrote:

I am also curious how in a double-blind trial it comes about that someone in the control is given the test drug.

On 2021-04-26 03:17:28, user YingYing Irene Wang wrote:

The Journal of Deaf Studies and Deaf Education - Accepted for publication, DOI: http://doi.org/10.1093/deaf... --- online publication is under process. It will be online shortly. Thanks.

On 2021-05-18 05:55:12, user YingYing Irene Wang wrote:

https://academic.oup.com/jd... accepted and published

On 2021-04-27 09:15:58, user Ramy Ghazy wrote:

This manuscript describe the geospatial distribution of under-five mortality in Alexandria Egypt, moreover, we identified the main determinant of under-five mortality. We hope to help the health authority and stakeholders to decrease future increase in U5M.

On 2021-04-29 18:57:59, user Kirsty Short wrote:

Yes it has been accepted at CID https://pubmed.ncbi.nlm.nih...

On 2021-04-30 14:31:06, user Gustavo Bellini wrote:

congratulations on the study! it would be interesting if the dose of cholecalciferol and calcifediol used was reported. patients supplemented with Colecalciferol may have had less protection because they were supplementing with low doses, which were not sufficient to raise the levels of 25OHD to the ideal range, so that vitamin D performs its immunomodulatory functions at maximum level. it would also be very interesting if 25OHD levels were reported in the supplemented groups and in a sample from the control group.

it is also important to note that a daily dose of around 5,000 IU (person weighing> 50 kg) of cholecalciferol will cause the 25OHD levels to gradually increase and stabilize at around 50ng / ml only after 4 months. on the other hand, an attack dose of 600,000 IU of cholecalciferol in people with low levels causes the 25OHD levels to rise in 3 days to the optimum range. the level starts to drop after 15 days, and in order to stay in the ideal range, a daily (5,000 IU) or weekly (35,000 IU) supplementation with realistic doses should be started. if supplementation is not done continuously, the 25OHD levels fall back to around 20ng / ml in a 2-month interval.

• Daily oral dosing of vitamin D3 using 5000 TO 50,000 international units a day in long-term hospitalized patients: Insights from a seven year experience<br /> https://doi.org/10.1016/j.j...

• Effect of a single oral dose of 600,000 IU of cholecalciferol on serum calciotropic hormones in young subjects with vitamin D deficiency: a prospective intervention study<br /> https://doi.org/10.1210/jc....

On 2021-05-11 13:08:35, user Tomas Hull wrote:

There was no placebo group... <br /> If the same study was among the unvaccinated frontline health care workers, dealing with SARS-CoV2 patients, wouldn't most of them have at least detectable IgG and IgM titers??? <br /> Why not test the same group of people again 2-3 months later and see what the antibody titers are, if detectable at all...

On 2021-08-11 14:46:17, user Richard Bruce wrote:

This is a very informative study. The methods do not say how testing for infection was handled to ensure uniformity of testing frequency between the different cohorts. Given the retrospective nature, there may be a selection bias. If we assume that vaccination reduces symptoms (which is reasonable given many data points including this paper), we can also then assume that subjects will seek testing more frequently when symptoms are present than in the absence of symptoms. Therefore, given that unvaccinated will be more likely to experience symptoms following infection, unvaccinated subjects are more likely to receive testing when infected. This will bias the infection rates but should leave the hospitalization/ICU rates unchanged.

On 2021-08-12 21:07:51, user Sam Wheeler wrote:

What about Johnson Johnson = Janssen vaccine? And Sputnik V vaccine?<br /> Janssen recently said their vaccine gives better protection after 8 months compared to 1 month after vaccination. And Janssen was approved based on efficacy after only 2 weeks. So Janssen may be much more efficient than previosly thought!

On 2021-08-11 20:10:04, user nullcodes wrote:

State of residence seems like too big of a geographic area to use as a match criteria. Cities and rural may have been a better criteria. The rollouts within states were quite varied.

On 2021-08-11 15:23:39, user Rene Reeves Brandon wrote:

The study included a cohort of unvaccinated individuals, but only reported on outcomes of individuals fully vaccinated with either of the two vaccines. What did the data on the vaccinated reveal in comparison to the vaccinated regarding previous infection, illness, hospitalization, and death? That data is necessary to share, especially as mandatory vaccines are being discussed in several states.

On 2021-08-14 17:36:32, user Matthias von Davier wrote:

Overclaiming, or the use of straight-lining is another option, as are other types of response biases.

In addition, the level of hesitancy for self-reported PhD (doctorate in the questionnaire) is at the same level of hesitancy seen in the group that chose to not answer the education question (missing education information also has 23.9% vaccine hesitancy).

On 2021-08-17 09:28:07, user One bird one cup wrote:

"Additionally, we assume the survey was completed in good faith." .... The assumption is what bothers me here. The people responding to a survey on Facebook aren't necessarily representative, as they're self-selected. This does not inspire confidence. Who's to say the respondents answered honestly about their education level? In addition -- apparently those who live in communities who were largely for Trump in 2020 appear to have been more vax-hesitant. I'm not a statistics person so I can't judge how the authors adjusted for this. But I feel hinky about it.

On 2021-08-14 17:57:29, user Hemaka Lakmal wrote:

How did you calculate the sample size required? How did you fix 282 individuals are enough for the test?

On 2021-08-16 05:24:54, user GT250ZX wrote:

I would take this research with a bit of grain of salt given the sample size of 282 observations. A much better study has been performed in Peru using 400,000 samples; the alleged level of efficacy is 50% per : https://www.reuters.com/wor...

On 2021-08-15 17:21:01, user carbsane wrote:

Can someone PLEASE explain to me how there can be 850 cases of COVID among the placebo group through March 13th if most of that group was subsequently vaccinated?? <br /> According to Pfizer's website they began unblinding and vaccinating in December (pretty much after the EUA), as they reported that as of Jan 29th 3,624 placebos had been FULLY vaxxed. Their last reported numbers (before dropping the information from their weibsite) were on Feb 24th by which time 16,904 had received at least one dose of vaccine.

On 2021-08-18 18:30:23, user Steve Kirsch wrote:

There were two people in the placebo group who got the drug after the unblinding. The paper never talks about the cause of death from those two people. This is EXTREMELY important. Does anyone know?

On 2021-08-22 00:51:09, user Anthony Loera wrote:

Supplementary data shows deaths between Vaccine people and controls are close to even<br /> https://www.medrxiv.org/con...

On 2021-08-06 22:23:54, user Ewin Barnett wrote:

The government of Scotland reported that 5,522 have died as a result of being vaccinated. No other data released like what percentage had comorbidities or were low on vitamin D at their time of admission to hospital.. No data released as to the appropriate percentage of the national population had been vaccinated. For a nation of about 5.5 million, this represents at least 0.1% risk.

On 2021-08-07 20:35:21, user vinu arumugham wrote:

Table S4 shows 4 deaths in the vaccine arm and 1 death in the placebo arm due to cardiac arrest. <br /> The probability that this outcome is a chance occurrence is 1.5%.

(((21999÷22000)^21996)×((1÷22000)^4)×(22000!))÷(21996!×4!) =0.0153 or 1.5%. <br /> So 98.5% chance that the vaccine CAUSED the excess cardiac arrest deaths.

Table S4 also shows 1 excess COVID-19 related death in the placebo arm.<br /> So to prevent 1 COVID-19 related death, the vaccine causes at least 3 deaths due to cardiac arrest.

On 2021-07-29 20:45:15, user Robert Earnest wrote:

So why are they recommending a booster?

On 2021-07-31 20:05:53, user I. Bokonon wrote:

Where are Figures S1A and S1B?

On 2021-08-01 12:27:54, user Oscar Perez wrote:

Table S4 shows 32 deaths among the vaccinated. 33 among the placebo. The vaccine is safe and effective. ????????????

On 2021-08-02 23:49:42, user Maria Knoll wrote:

It would strengthen the duration of protection analysis in the table in Figure 2 if the potential for confounding by age, country and case ascertainment could be ruled out. The VE differed by age group and country (not statistically – wide 95%CIs), but I do not think they were adjusted for. Calendar time may also be a potential confounder if the 4+m period is capturing more post-holiday cases (Jan) while months 2-<4m period is capturing more pre-holiday (Nov). Changing rates in testing might also impact VE: if testing increased in the latter period due to increases in travel and as a result picked up more asymptomatic cases, that would lower its VE because VE is lower for asymptomatic infection than for symptomatic (99% of all cases >7d post dose 2 were non-severe but %symptomatic by period is not described). Also, if there was some unblinding (those with reactions may have correctly guessed they got the vaccine), vaccinees might put themselves at more risk (i.e., travel for the holidays) than placebo recipients which would mean vaccinees would have higher chance of infection (which would lower VE). It would be nice to see a sensitivity analysis performed on a restricted set of participants to try to remove some potential confounding, such as restrict to US only (which were 76% of the participants), restrict to adults (perhaps age 50+ or pick some narrower age range than the current age 12+), and adjust by calendar time of infection. Also describe the testing and positivity rates and proportion symptomatic among cases stratified by vaccinees/placebo and follow-up strata (i.e., 7d-<2m, 2m-<4m, 4m+) to see if case detection was similar across intervention groups and constant over the time periods.

On 2021-08-04 12:33:07, user Will Helm wrote:

the 14+15 deaths are not vaccine related, bur are "normal demographic" deaths.<br /> A study of Pfizer for European nations based on Eudravigilance values gives a ratio of 15 vax-related deaths per million doses. We can assume it's the same thing for this study. So, for 22'000 who received the shots, statistically there's no death possible due to the jab.

On 2021-09-18 19:05:19, user OBS wrote:

These long-term results from Pfizer's clinical trial are quite informative- does anyone know of a corresponding preprint from Moderna containing their long-term (i.e. 6 months or so) clinical trial results, particularly regarding safety (total deaths, adverse events, etc.) Moderna has said it's efficacy at 6 months was 93% (a tiny bit better than Pfizer), and all 3 COVID deaths by 6 months were in the placebo group (also better than Pfizer's result of 1 COVID death with vaccine vs. 2 COVID deaths with placebo). This is not at all surprising considering that Moderna's vaccine dose is over 3x higher than Pfizer's. But what about Moderna's results for total deaths, how do they compare to Pfizer's? Surely, so many people here would like to know.

The following is stated in an article from 3 days ago on Moderna's website:

"Additionally, the Company shared a new analysis of follow-up through 1 year in the Phase 3 COVE study suggesting a lower risk of breakthrough infection in participants vaccinated more recently (median 8 months after first dose) compared to participants vaccinated last year (median 13 months after first dose). Manuscripts summarizing both findings have been posted to preprint servers and will be submitted for peer-reviewed publication."

However, no such preprint from the COVE study seems to show up- does anyone know how to find it?

On 2021-08-16 19:12:41, user Paul Wang wrote:

Abstract(results) needs to be fixed: first sentence gives the exact same name to 3 of the 4 subgroups

On 2021-08-18 14:12:25, user Jessica Lyne wrote:

I am curious if Dymista alters the testing results.

On 2021-08-20 22:49:31, user kdrl nakle wrote:

Poorly written paper, in bad need of rewriting.

On 2021-08-21 12:15:04, user muhamad khairulbahri wrote:

This preprint is now published at:<br /> https://wjst.wu.ac.th/index...

On 2021-08-22 14:07:20, user JB wrote:

have been approved for use in humans since December 2020

They have been authorized, not approved.

On 2021-08-22 19:34:47, user ingokeck wrote:

There is another issue with the article. You do not describe the data collection for the control group. Has this been done with the same preprocessing, using the same PCR arrays, i.e. are the values that the RT-PCR generates comparable even though there is one year difference in the sampling? Did you use an internal control for human DNA so you know that the sample collection was the same every time to get normalized RNA loads independent of the sampling procedure?

Even if the value generation is comparable, why do you think you can compare the Ct values from the D614G linage to the delta linage, given that it seems delta is more infectious than previous variants?

On 2021-08-23 10:16:41, user David States wrote:

Figure 1, panel C is key to much of the discussion. I’d like to see the actual data points as well as the fit curves. Also the units on the x-axis, genome equivalents per mL, are calculated from Ct using a proprietary undocumented formula and are not used elsewhere. I’d like to see a second x-axis labeled in Ct.

On 2021-08-23 21:24:22, user Stuart wrote:

Published in JCI Insight, here: https://insight.jci.org/art...

On 2021-08-26 04:22:25, user brisalta wrote:

The paper does not clearly state which variant the subjects were previously infected with. If that data is available it may be useful to include that information.

On 2021-08-26 04:25:01, user glit00 wrote:

Perhaps I'm overlooking something obvious, but why is there no table that shows hospitalisation OR for model 3?

Also, should table 4b be labelled as "model 3" and not "model 2"?

Finally, I think drawing conclusions on "model 3"'s p-values is a bit cheeky...

On 2021-08-27 13:14:22, user Ash Guirgues wrote:

To which journals is this article being submitted

On 2021-08-28 22:44:31, user Business wrote:

Have you considered comparing Covid-19 naïve vaccinated vs unvaccinated and Covid-19 previously infected vs Covid-19 naïves?

Also, is the data available for further analysis?

On 2021-09-11 16:32:55, user Chadwick wrote:

Red Flags all over the place, like there are 2.5x more immunocompromised in normalized comparison groups, being immunocompromised makes re- or breakthrough infection LESS likely, Economic class swings between comparison groups, being wealthier makes infections more likely...

On 2021-09-18 21:17:19, user Mike wrote:

This study did an awesome job! One of the reason it didn't have the political bias rather letting the science flow instead of putting on a show to sway people in a particular direction to fit a particular narrative. I predict more studies will bare this out, that natural immunity is better than vaccines but it doesn't mean people shouldn't get vaccinated if they want to but shouldn't be forced! Israel is an example of weakness with the Pfizer vaccine, most likely the other have this weakness too. One of the weaknesses of this vaccine and others is the fact that it's the same, viruses change. This is why the delta variant spreads easier than previous versions.

Back in August almost 60 percent of the hospitalizations in Israel were people at least 60 years old and fully vaccinated! Israel's government a great believer in vaccinations will be going on their 4th booster! I don't see a high rate of re-infections of those who already had Covid along with hospitalizations, but I do believe Covid is here to stay and re-infections will become common and some people will have major health issues with it. People 18 or younger which total so far 74 million Covid infections, 362 have died due to Covid complications.

On 2021-09-20 23:38:51, user BaboliDaboli wrote:

The study states: "Symptoms for all analyses were recorded in the central database within 5 days of the positive RT-PCR test for 90% of the patients, and included chiefly fever, cough, breathing difficulties, diarrhea, loss of taste or smell, myalgia, weakness, headache and sore throat." I understand this as follows: 90% of recorded symptomatic COVID-19 cases in the study were first recorded as positive on RT-PCR test. That would mean that any correlation between recorded number of positive RT-PCR tests, recorded symptomatic COVID-19 outcomes, and recorded COVID-19 related hospitalization outcomes may stem from the fact that positive RT-PCR test was a prerequisite for any other outcome to be recorded at all. As there were no fatal outcomes recorded, most hospitalizations recorded in the study might have been due to mild symptoms and a previous positive RT-PCR test (study fails to present the breakdown of hospitalizations by disease severity or duration of hospitalization). An earlier study of the link between hospital load and increased COVID-19 mortality in Israel (Rossman, H., Meir, T., Somer, J. et al. Hospital load and increased COVID-19 related mortality in Israel. Nat Commun 12, 1904 (2021). https://doi.org/10.1038/s41467-021-22214-z) found that between July 15th 2020, and January 1st 2021, on average, almost 60% of people were hospitalized while presenting mild initial clinical state. So, if most breakthrough infections or re-infections in the study never progressed past the mild clinical state, and if such patients wouldn't even be considered for hospitalization in Israel without a positive RT-PCR test, it is quite possible that all study outcomes depend directly on people taking a RT-PCR test and testing positive. While it may be difficult to assess and account for the difference between vaccinated and previously recovered people in terms of their inclination towards taking the test in case of mild or non-existent symptoms, any possible bias in terms of testing policies in Israel should be addressed and accounted for in the study. An example of such potential bias can be found on the Israel Ministry of Health "Testing for COVID-19" webpage (https://www.gov.il/en/departments/general/corona-tests) which states: "As a general rule, save for few exceptional cases, it is not necessary for confirmed patients or recovered patients to take a swab test for coronavirus, unless there is clinical suspicion for repeated infection with the virus." As clinical suspicion depends on severity and combination of symptoms typical for COVID-19, and as the set of symptoms related to delta variant seems to differ slightly from the set of symptoms related to previous variants, some or maybe even many reinfected people never got tested by RT-PCR as they had no symptoms or had only mild symptoms that never progressed beyond that. If that were the case the number of reinfections and related outcomes might be significantly underestimated in this study.

On 2021-10-04 13:50:29, user Steve Vlad wrote:

To the study authors:

As an epidemiologist, if I was reviewing this paper for publication I would send it back to you for major revisions or reject it outright. I would not even bother looking at the results.

The major issue is that you have conditioned study group entry by an event that happens at the end of the study. I.e. you have created a cohort of unvaccinated persons who must remain unvaccinated throughout the study. This is guaranteed to introduce selection bias, more specifically immortal-time bias. This further guarantees a biased estimate. This topic has been written about many times. Cf any of many articles by Sammie Souza at McGill.

Imagine someone in your unvaccinated cohort. Soon after the initial study date they develop an infection. 5 weeks later they have recovered and decided they should have had the vaccine, so they get one. Because you have insisted this group remain vaccine free you throw them out of the group and you lose their data. You have just thrown out an infection. Do this just a few times and it is guaranteed that your ‘vaccinated’ group is not reporting as many infections as it actually experienced. This easily accounts for the effect you report.

Note that this does NOT happen with the fully vaccinated/boosted group who must receive all vaccinations prior to study entry. You capture each and every infection with no drop out. Thus you’ve created a situation where you have non-random drop-out between the groups. That is selection bias.

To get around this problem you MUST use methods such as Cox proportional hazards modeling with time-varying exposure variables so that persons can move between cohorts based on exposure to the vaccine during the study period.

Hope this helps.

On 2021-08-04 02:30:29, user Deplorably Black wrote:

Interesting. Does this still apply considering<br /> the new variants?

Has a study been conducted as to the vaccines effect on long COVID?

I suffered from daily headaches post COVID for 8 months. They stopped immediately after vaccination.

I know several others with the same experience.

That in itself made vaccination post COVID worthwhile for quality of life.

On 2021-08-09 20:32:15, user KS wrote:

I haven't scrutinized this paper but even if all the results are accepted, the one-line "Conclusions" at the beginning is highly problematic without qualifiers. "Unlikely to benefit"? This study is limited, so the conclusion can't be so broad. Are elderly or long-haulers unlikely to benefit? You've got a 42 day window (seems arbitrary) so if you got the disease 6 months prior are you unlikely to benefit from the vaccination? The study doesn't address any of these things, yet makes a huge leap in its conclusion. This is a pre-print so PLEASE make a conclusion that fits your experiment and data. Because it's likely the only thing the general public will read and it will become the basis for more misinformation.

On 2021-06-09 18:09:15, user Paul Cwik wrote:

Peer Review in this case does not mean that peers reconduct the experiments. It simply means that others (with suitable credentials) have read and accepted the paper as having correctly followed the scientific methods. In other words, they are simply looking for errors in the paper, not re-doing and confirming the results.

On 2021-06-12 20:06:10, user Bill Wilson wrote:

The sample size could be larger but it is the best “science” we have so far.

On 2021-09-11 12:19:37, user William Brooks wrote:

This study finds similar results to studies looking at infections among South Asians in England [1] and foreign workers in Kuwait [2]: lockdown heightened the curve for groups with more crowded living conditions. The results also agree with those of the nearest thing we have to a lockdown RTC: higher secondary attack rates in asylum centres that mass-quarantined all residents in Germany [3].

Despite this, the authors claim lockdowns work. Like a pharmaceutical intervention, for a non-pharmaceutical intervention to be said to work, the intervention group (e.g. NY, CA) has to show significantly lower mortality and morbidity than the control group (e.g. FL, SD), which isn’t the case [4]. Also, for extremely authoritarian interventions to justify their many negative side-effects, hospitals in the control group would need to be overflowing like the models predicted, which has never come close to happening.

[1] https://doi.org/10.1016/j.e...<br /> [2] https://doi.org/10.1186/s12...<br /> [3] https://doi.org/10.1101/202...<br /> [4] https://doi.org/10.1101/202...

On 2021-09-12 05:10:38, user kdrl nakle wrote:

This is really a weak study. It jams together 4 vastly different vaccines and then reports without differentiation. So what do we know out of that? It could be one has 100% protection and another 50% and we would claim it is 75% overall?

On 2021-09-12 06:41:02, user kdrl nakle wrote:

Air outlets versus inlets. Very important though not surprising information. The rest of this paper is also important info.

On 2021-09-14 06:20:47, user Mike Hawk wrote:

A friendly grammar edit to the study, in the abstract section. Instead of, "One possibility is that such negative outcomes...while some tend respond with empathy (feeling what others feel), others tend respond with compassion (caring about what others feel)", I suggest that it should have been "One possibility is that such negative outcomes ..: while some tend to respond with empathy (feeling what others feel), others tend to respond with compassion (caring about what others feel)."

On 2021-09-14 08:36:03, user Dharshana Kasthurirathne wrote:

one of the conclusions is that the younger people are 17 times more likely to get hospitalized if they are not vaccinated, compared to those that are fully vaccinated. however, if you think of the time period compared (jan-jul), those who were unvaccinated may have had much higher chance of encountering the virus (simply cos they were in that status for a longer time) compared to those who are fully vaccinated (who were in that status for a much smaller time period). it's safe to assume that those who are fully vaccinated (particularly younger people) changed to that status quite recently. so is it correct to do such a population wide comparison without normalizing for the time since acquiring the vaccinated or unvaccinated status?

On 2021-09-17 16:24:24, user Thom Davis wrote:

"Antibody neutralization titers against B.1.351 and P.1 variants measured by SARS-CoV-2 pseudovirus neutralization (PsVN) assays before the booster vaccinations, approximately 6 to 8 months after the primary series, were low or below the assay limit of quantification" is the key "real information" in this synopsis. All others are conjecture. If it isn't measurable, it isn't there.

On 2021-09-21 11:11:33, user 4qmmt wrote:

Would any of you agree that rate of myocarditis/pericarditis due to the vaccine in youth is a) unknown and b) higher than the available data suggest?

On 2021-09-10 16:16:11, user bee researcher wrote:

To clarify, this work is not comparing the risk of myocarditis in vaccinated individuals with the risk of hospitalization in similarly aged COVID-positive individuals, but rather an age-matched demographic regardless of COVID infection status. Is that correct?

This seems misleading in terms of risk assessment, because it's comparing the risk after a specific event (vaccination) with the background level of risk over certain periods of time. Yet active spread of COVID appears likely to continue for at some level for years, and the risk of hospitalization in COVID-positive individuals in this age group is much higher than the risk of vaccine-related myocarditis. Indeed the risk of COVID-related myocarditis is higher in this age group than the risk of vaccine-related myocarditis. If eventual infection by a now-endemic COVID-19 is incredibly likely, than it seems more informative to compare the risks associated with such an infection with the risks of vaccination.

On 2021-09-10 16:30:40, user Roger Seheult wrote:

Why did you choose to compare vaccine related CAE with COVID hospitalizations? Why not compare Vaccine related CAE with COVID CAE? Are we comparing apples to apples? Hospitalizations in pediatric population (especially before delta) was rare from COVID-19 for any reason and most vaccine CAEs do not require hospitalization as I understand.

On 2021-09-10 19:11:30, user David Goldberg, MD, MSCE wrote:

Although the scientific question that is being address is an important one, I have concerns about the methodology used to adjudicate the outcome. In similar circumstances (e.g., the FDAs Mini-Sentinel Initiative), complex clinical outcomes like this (e.g., acute liver failure) were adjudicated independently by two experts, with a third person serving to break any ties. That seems not to have been done in this study, as there was only one cardiologist involved. Secondly, the clinical data to adjudicate the outcome of myocarditis seems to be insufficient in many cases. Although one could argue "this is the best data we have" sometimes that is not good enough. When the question is so important and politically charged, incomplete/invalid data is sometimes worse than no data. Unless the authors can have two-party adjudication with record review, and classification using standard techniques (e.g., definite vaccine-induced myocarditis, highly likely, probable, possible, not) then there are major methodological concerns with the outcome, and the overall validity of the study.

On 2021-09-10 21:38:17, user anime profile picture wrote:

This study completely misses the point of young kids getting vaccinated. COVID is infectious. Meaning when someone gets the virus, it can be passed on. Whether or not they are at high risk relative to the adverse side effects, they should be vaccinated to reduce the probability of older, more at-risk people from getting it. In short, young boys should get vaccinated to protect them, their parents, their teachers, and their grandparents. Consult with your doctor of course. I am no medical professional, but I understand that a vaccine does more than protect the person being vaccinated.

On 2021-09-29 01:16:54, user Alberto wrote:

So on 2 September the total severe cases was 629 (hospitals and ICUs at the verge of collapse), and the estimation if 0% of the population had been vaccinated is that there would have been 5182 severe cases in the country. And this during the summer. I don't have the figure at hand of how many severe cases were on 2 September 2020, when 0% of the population was vaccinated, but it may have been around 250-300? It's hard to know what exact effect mass vaccination is having that leads to these kind of absurd results, but it would be worth looking at it in detail.

On 2021-10-14 23:01:32, user doseofcommonsense wrote:

Public Health has estimated Delta's CFR to be 0.096 %.

On 2021-07-31 13:57:16, user Richids Coulter wrote:

Data from the UK shows an almost complete decoupling between cases and hospitalizations/deaths - this won’t pass peer review because it’s complete and utter nonsense, par for the course for Fisman and Tuite who have been almost completely wrong with their modelling the entire pandemic.

On 2021-09-02 12:51:16, user David Curtis wrote:

I have a few comments.

Figure 2B suggests there is quite a lot of inflation of the test statistic.

Some genes will have an excess of variants in controls rather than cases. This means it makes sense to plot a signed log p (SLP), rather than a minus log p (MLP), in which a negative sign is given if there is a an excess of variants in controls. This is what I did in my study of the first 200.000 exome sequenced subjects:<br /> https://journals.lww.com/ps...

Plotting the SLP rather than the MLP makes it easier to detect possible problems with the analyses, such as inflation of the test statistic in one direction.

The result for SCL2A1 is based on a total of 52 carriers. As far as I can work out, 10 of them are cases and 42 of them are controls. So the claim that SCL2A1 is involved in depression aetiology is really based on the fact that damaging missense variants are observed in 10 cases. With such small numbers, regression analyses may give unreliable p values. In fact, I did a simple Fisher's exact test (not including any covariates) and this yields a p value of 4.027e-05, which falls just short of exome-wide significance.

I wonder if the test statistic is inflated because there are genes in which there is a slight excess of variants in cases but the methods used tend to produce p values which are too low, because of small numbers, as seems to be the case for SCL2A1.

The other thing I would say is that the estimated OR for SCL2A1, 6.01, does seem to be surprisingly high. I would not have expected that damaging missense variants, grouped together as a class, would have such a large effect.

On 2021-09-04 04:21:48, user lifebiomedguru wrote:

Please describe how the groups "vaccinated" and "unvaccinated" defined? Were patients who were vaccinated consider "unvaccinated" until 14 days after their second dose for Moderna of Pfizer products, per CDC's definition? This would clearly bias the main result in favor of your conclusion. The fact that NYT cited this work as a subtext and the Editor chose your conclusions as their title confirms to me that this publishing preprints prior to peer review may be doing some damage to the long-term credibility of science.

On 2021-08-02 17:30:40, user Jeremy wrote:

Wow, this study is pure garbage.

Half of the study's age demographic couldn't even be vaccinated during the duration of the study since approval for 12-15 year olds came after it had ended. Not to mention that the other half only had the vaccine available for 25% of the study duration.

On 2021-09-05 16:07:08, user JimmyJoe6000 wrote:

Someone posted an inception to date chart using daily deaths for the two groups of countries. I can't see to find in in any of the articles like this. It mentioned John Hopkins along with this link. <br /> Anyone have the link to the chart?

On 2021-09-07 01:37:42, user Simon Turner wrote:

This paper has now been peer reviewed and published at BMC Medical Research Methodology:

Turner, S.L., Forbes, A.B., Karahalios, A. et al. Evaluation of statistical methods used in the analysis of interrupted time series studies: a simulation study. BMC Med Res Methodol 21, 181 (2021). https://doi.org/10.1186/s12...

On 2021-09-07 14:39:30, user Brett Tyler wrote:

Interesting approach to use Kallisto. I have a couple of questions. 1. How do you account for variability in the amplification efficiency of different ARTIC amplicons. 2. How do you account for the numerous reads that match non-informatic regions of the genome (i.e. those with no informative SNPs)? 3. How do you account for reads that match multiple different variants?

On 2021-09-07 18:29:08, user Eileen Doyle wrote:

Eugene uses a popPK model for fluoxetine concentrations in breast milk to predict systemic concentrations (the Tanoshima 2014 paper from which the model was developed states, "the objective of this proof-of-concept study was to develop a simple pop PK model predictive of FX and NFX milk concentrations without referring to plasma concentrations..."). While Tanoshima concludes that the estimates were consistent with those of the plasma/milk-based pop PK model, the authors are comparing the milk estimates, not the plasma estimates. <br /> Additionally, the author states the unbound fraction of fluoxetine is 0.94. Fluoxetine is 94.5% protein bound [Prozac(R) label], giving an unbound fraction of 5.5%.

I have contacted the author with this comment as well.

On 2021-09-08 07:34:58, user John Connolly wrote:

This preprint has now been published in the peer-reviewed journal 'Parasites and Vectors':<br /> https://doi.org/10.1186/s13...

On 2021-09-09 18:08:45, user Jason Howard wrote:

Overall, I applaud the authors for writing this manuscript. It's valuable data for public consumption. That said, I think the report would be more impactful if they also recorded which vaccine the vaccinated subjects had.<br /> It would be great if the authors mentioned what internal control gene (ex human RNAse P) the testing center (Exact Sciences Corporation,) used. I also think the authors should remind the readers that a lower Ct value corresponds to a higher viral titer.<br /> One item to mention is that some medical personnel do a better job of collecting a nose swab sample. The quality of collection can affect the Ct value. The authors should also consider citing the source of the estimated delta variant prevalence mentioned in the abstract.

On 2021-08-03 06:27:40, user LaurieAnnaT wrote:

https://www.medrxiv.org/con...

This other study indicates that while vaccinated folks may be just as contagious as the unvaccinated initially, their viral load rapidly declines such that they ultimately will infect fewer people on average.

On 2021-08-03 11:19:56, user TBV wrote:

1. Since we don’t know characteristics of patients in each arm, these results could simply reflect the vulnerable, with weaker immune systems, being more likely to be vaccinated.
2. The authors throw out roughly 1/3 of the observations in an already small study because viral loads are low. Isn’t it possible these were mostly vaccinated people? So, by cutting off patients to only those with a fairly high viral load we generate the result that vaccinated people in the study have a high load

On 2021-08-03 14:16:09, user Dimich wrote:

Since the conclusion about the infections is based on PCR testing, it is pointless. PCR tests do not check if the person is infected or not, but confirm presence of SARS-CoV-2 genetic material in the sample, which may not be associated with the infection, but can be a randomly inhaled viruses or leftover of previous asymptomatic infection.<br /> Two references on PCR testing subject:<br /> https://www.nejm.org/doi/fu...<br /> https://www.journalofinfect...

On 2021-09-10 17:44:10, user Vivi Wacko wrote:

Please note that this manuscript has been <br /> published in the Lancet Infectious Diseases :

https://www.thelancet.com/j...

On 2021-12-06 16:24:28, user Hank Black wrote:

What cycle levels were used in the PCR tests that were used to determine infections? If those levels we’re above 24, then this paper is irrelevant. If the authors can identify people who had symptomatic Covid disease, and compare that number to current persons symptomatic with Covid disease from the new variant, then this paper might have merit.

On 2021-12-06 18:05:54, user FACAGIRL wrote:

What were the CT values for the PCR confirmed cases. I ask because PCR test only test for presence of virus and not infectivity - yes? I found the following CEBM/Oxford systematic review on this and the detail suggesting a lower CT value is better to use as proxy for infection - was based on cultured and PCR samples. Reinfection would be subject to the same limitations associated with PCR tests.

Thanks

On 2021-12-14 19:48:50, user nitin wrote:

how you have made this frequency graph

On 2021-12-21 06:29:36, user Diego Hernandez wrote:

I am still saddened how little seroprevelance data is available at CDPH. I had my public records request rejected 3x for Megha Mehrotra's inaccurate Seroprevelance study that was cancelled in July 2021. Cancelled due to routine blood screening cancellation yet, it was not included as part of Tomas Aragon's public health order 1 Day after canceling CDPHs seroprevelance data releases.

I still do not have your modeling for the studies CDPH released. I doubt sending another 3 FOIAs will get me the results.

When I asked for updating Seroprevelance studies in California beyond August 2020 you linked me back to CDC interactive dashboard.

In August 21' you co-authored a paper with seroprevelance data back to August 2020.

The policies issued through this pandemic are not in line with the data available. If policy is being coerced on people it has to be within reason, knowing a VE drop off can be at 90 days or sooner, why force persons into destitution of employment for refusal of vaccinations.

The trend points toward seasonal vaccinations in late Sept and boosters in December... But that's not the policy and transparency CDPH offers the public.

I've moved on to other topics of interest but I have lost faith in transparency at CDPH in decision making.

On 2021-12-22 02:45:31, user Renee, the cooking RD wrote:

It would seem that the fact that the plant-based nature of the intervention diet might have been a confounding variable and possibly the major contributor to the positive effects. Lots of research already shows that plant proteins are a lot kinder to kidneys than animal protein.

On 2021-12-23 14:40:32, user Margalit wrote:

Totally feel vindicated as I (like many others) suggested taking Vitamin D in April 2020. https://theprepared.com/blo...

However, as outlined there, and by many others, e.g. the UK bio bank, there has been a link between race and severity in many countries. In the UK study, the effect of Vitamin D disappeared once ethnic background was taken into account.

I am glad you controlled for SES as a crude 3 step factor. But it may not be enough. Also in Israel, people with dark skin are both discriminated against, experience lower SES, and are hence predicted to be at lower Vitamin D levels. Is there a way to subdivide by ethnicity better than Ultra-orthodox, general and Arab, but account in "general" for Ashkenazi, Sephardi, Oriental, and African origin? I just think part of the effect - like in the UK and US - may be due to skin color darkness, discrimination, SES and Vitamin D deficiency being totally confounded.

On 2021-12-23 21:46:48, user Maxime Bedez wrote:

Hello,<br /> At page 4, it is stated that IC50 on Vero cells is 0.038µM and CC50 is 2.9µM. The reference is Fig. 1B. It is not clear, but largely implied by supplementary information, that it is Rodon data (page 7 of Supplementary).<br /> Rodon et al. have published here 10.3389/fphar.2021.646676<br /> In Rodon's paper, the IC50 is 60 and CC50 is 100 (0,06 and 0,1 in nM, page 7).

I am confused, where did I get it wrong ? Did Rodon do another identical experiment with different result ?<br /> I think it need clarification, as 100 and 2900 are really far appart.<br /> Thanks

On 2021-12-24 07:45:25, user Jeff H wrote:

So assume the results you like (high VE for recent vaccination) are causal, but hand wave confounders at results you don't like (negative VE for distant vaccination)? Science?

On 2021-12-24 21:34:35, user Robert Parker wrote:

So, these vaccines are, essentially, not effective against Omicron. The upside is that Omicron seems, at the moment, to be like getting a really bad cold. Very little hospitalization, and no deaths as far as I can find. This may be a Godsend. It is highly transmissible, with few bad effects. It may actually serve as a means to herd immunity, with few deaths. Hope springs eternal.

On 2021-12-28 14:49:56, user Bob Horvath wrote:

There is a typo in the confidence interval reported here: "36.7% (95% CI: 69.9 to 76.4%)", since the confidence interval needs to incorporate the value of 36.7%.

Also, this paper defines vaccine effectiveness (VE) as protection against infection. As can be seen from some of the tweets on this study, that is confusing readers who don't realize the EUA granted in the U.S., for example, is based on definitions of effectiveness related to hospitalization and/or death. It would be very helpful to many to have this even very briefly clarified in your paper, that, for example, even if the VE was 0% (or even negative, as some of the threads here claim is being shown after 90 days) according to the definition used in this study, as long as it had more than 50% effectiveness against hospitalization and death, that it would still be used in the U.S.

On 2022-01-13 13:10:50, user David Knight wrote:

Scotland's latest official public health real world data tallies up with the negative effectiveness found by the scientists that carried out this study.

https://publichealthscotlan...

See Table 15.

People who had only 2 jabs were almost 3 times more likely to catch Covid in the week 25th Dec-31st Dec than the unvaccinated (who were similar to the 'boosted')

Unvaccinated 1,555,449, cases 20,276, 1.3%<br /> 2 Doses 1,522,961, cases 54,727, 3.59%<br /> Boosted 2,429,498, cases 30,222, 1.24%

But if you are boosted you appear to be at least 4 times less likely to be hospitalised or worse from Covid, than the 2 jabbed/unvaccinated. See tables 16 and 17. So there still is a case for the vaccines

On 2021-12-25 04:08:33, user Nick Bauer wrote:

Figure 1 legend should explain the colors better because it's hard to interpret otherwise.

On 2021-12-25 09:26:15, user ReviewNinja wrote:

Interesting samples.<br /> One important flaw: you cannot compare Ct values from PCRs performed with different laboratory workflows as is the case here. The Abbot RealTime test for example tests 2 targets in the same channel, which might give you an earlier Ct. Also pre-PCR worklfow matters.

On 2021-12-27 02:04:58, user vepe wrote:

I could be missing but after reading the study it looks like you have included both vaccinated and unvaccinated in the post-positive test(i.e. infection) cardiac adverse events.<br /> have you considered stratifying the post-positive test group by vaccination status?

That way, we may assess the actual risk associated with the vaccines when it comes to cardiac issues

thanks for your work btw

edit, to clarify, the risk associated with vaccines is: <br /> risk of getting an adverse event after getting jabbed + risk of getting an adverse event after breakthrough infection. Without stratifying the post-infection results based on vaccination status, then we can't estimate the second part of the equation

On 2022-01-08 03:47:33, user Robyn Chuter wrote:

Also, deaths occurring in people who developed myocarditis due to a breakthrough infection should be distinguished from deaths of people whose myocarditis was not related to breakthrough infection.

This would help identify whether ADE or related phenomena are contributing to myocarditis.

On 2021-12-27 23:17:44, user Ralph Bertram ?????????????/ ???? /???? wrote:

Dear readers, the supplemental html files are available here

https://c.gmx.net/@63828482... <br /> (all nodes)

https://c.gmx.net/@63828482... <br /> (only pos)

On 2021-12-28 09:32:57, user Joe Random User wrote:

Table B clearly says that some / many of the participants had only just received their booster jab on December 2nd. The date of the private gathering was "early December" and the genetic sampling results were received by December 8th. So most likely the private gathering took place between December 2nd and December 7th.

Everbody knows that it takes 2 weeks for the antibodies to develop to their full potential after the third booster jab.

Since this article does not specify how many of the participants had not been fully vaccinated with the 3rd booser jab the data in the article is insufficient to learn anything about the omicron variant's resistance to the booster jab.

I recommend that the authors produce a revised paper where they more carefully describe the vaccination dates for the 3rd jab for all participants.

On 2021-12-28 14:23:53, user Zacharias Fögen wrote:

Dear Authors,

Thank you for this study, which clearly demonstrates that there is no IgA response to vaccination, thus not causing immunity to infection. Yet, irritatingly, you claim the opposite.

Figure 2F shows that there is no significant RBD-IgA after 2 vaccinations.

As for Spike-IgA, there is a wrong labeling in Figure 2E, as the "ns" should belong to the comparison "neg-ctrl vs. mrna 2 doses" and not to "covid-19 vs.mrna 2 doses" the latter being clearly significant, the former showing that the median of "mrna 2 Doses" is below the positive cutoff.

Furthermore, your "Baseline" mean in figure 2K is much higher (about 2,5%) than "negative control" in figure 2E (about 0%). Since both "baseline" and "negative control" are not vaccinated, this points to a selection bias for your negative control.<br /> Figure 2K also shows that there is no significant difference concerning "Baseline" and "2-4 weeks post dose 2". Yet, there is a significant difference between doses 1 and 2, as well as 1 and baseline.<br /> When comparing "baseline" and "mrna 2 Doses", "mrna 2 doses" is as high as "2-4 weeks post dose 2", which is not significantly different from "baseline" (Figure 2K).

So, there is no significant IgA (both Spike-IgA and RBD-IgA) after 2 doses of vaccination.

As far as the increase after 1st dose, but not after the second dose, this either points to an unknown bias, or it shows that multiple vaccinations do not increase IgA production, hinting at a lack of booster efficiency.

In Version 2 you had 6 month follow-up values in figure 1, yet in figure 3 the 6-month follow up (now figure 2) was removed. Why is that?

I kindly ask for the underlying data.

Greetings, Zacharias Fögen

On 2021-12-30 19:39:03, user Jesusswept wrote:

Study Funded by the Bill & Melinda Gates Foundation.

On 2021-12-30 20:48:47, user rick wrote:

Donating vaccines is a completely uninformed idea. There are plenty of vaccines. Latin America is more vaccinated than the U.S. Nigeria, on the other hand, is plowing expired vaccines into landfill, because nobody wants the stuff. Pfizer says they can make a lot more vaccine right away; they just need orders. If you want to make sure no poor nation is deprived you send money, not vaccines. If you are afraid that they will go hungry, you sent money for that too. You don 't mail them french fries.

On 2021-12-31 16:37:47, user Chris Holm wrote:

I think this part is very important. "We observed no difference in the LoS for patients not admitted to ICU,nor odds of in-hospital death between vaccinated and unvaccinated <br /> patients."

So, the unvaccinated doesn't spend more time in <br /> the hospital (except for those admitted to the ICU). And in any case, <br /> the unvaccinated are not more likely to die from covid. Good to know.

On 2022-01-02 19:41:20, user James Gator wrote:

Great preprint, I think clarifying what "early vaccinees" vs "late vaccinees" is a valuable addition. It's not immediately clear which time point they are early or late from

On 2022-01-04 08:01:45, user Cathy wrote:

It seems the only valid conclusion from this study is that immunocompromised patients who SURVIVE having covid have similar antibody levels. Not surprising, those that did not likely are the ones who died. You cannot make such a conclusion without measuring the antibody levels in both categories who died. Come on now, don't lead immunocompromised people to believe something you have NOT proven. I sure hope this gets revised before it gets released. It is dangerous.

On 2022-01-06 17:05:32, user SE Justus wrote:

Where/how do those who have recovered from the disease (naturally immune) fit into this study?<br /> Thank you.

On 2022-01-06 22:05:45, user Faithkills wrote:

Failure to include those with acquired immunity from recovery makes this of little use and exposes a likely bias of the researchers.

On 2022-01-06 18:42:10, user sd wrote:

Anyone interested in validating the NPRP criteria in their clinical setting please do and post your results here. Also see the published version in Prim Care Diab

On 2022-01-06 21:58:54, user zlmark wrote:

The interpretation the authors give to what they have actually calculated is highly misleading.

What they compute is the probability of a single transmission event in a *specific place*, whereas in order to estimate the costs of the policy, one needs to compute the probability of a transmission in *any one of the places* of the given type, which is several orders of magnitude larger.

Moreover, they completely ignore the compounding effect, though which even minor differences in R can lead to exponentially growing difference in the number of cases.

So no - 1000 people do NOT need to be excluded to prevent one COVID case - not even close.

On 2022-01-07 20:51:11, user Sam Miller wrote:

By now, we know that the transmission rate of omicron is high, regardless of vaccination status. Reducing transmission is a marginal, secondary goal of vaccine passport/mandates. Whether we think it is an ethical policy or not, the primary goal is to significantly increase the vaccination rate through carrot/stick motivators to prevent hospitalizations and health care system failure. Data from several countries have shown proportionally higher hospitalizations rates for unvaccinated. Although it may be difficult to quantize, a more pertinent question on policy efficacy would be "How much have these mandates/passports increased vaccination rates and reduced hospitalizations, and at what social capital cost?"

I think Aaron Prosser said in his Youtube interview with Vinay Prasad, MD, that the vaccination rate was 85% in his area. I wonder if he thinks that rate could have been reached without some type of vaccine passport/mandate policy? A recent Lancet study, "The effect of mandatory COVID-19 certificates on vaccine uptake," states that COVID-19 certification led to increased vaccinations 20 days before implementation in anticipation, with a lasting effect up to 40 days after. It concludes that "mandatory COVID-19 certification could increase vaccine uptake, but interpretation and transferability of findings need to be considered in the context of pre-existing levels of vaccine uptake and hesitancy, eligibility changes, and the pandemic trajectory."

On 2022-01-07 00:54:00, user Mark wrote:

The vaccination rate documented in TriNetX is only about 2%, whereas the reported vaccination rates during this time period12 indicate that most patients in the study population were likely to have been vaccinated.

Although partially mitigated by propensity matching, this is a huge limitation that makes it hard to separate the protective effect of vaccination (since it was essentially unreported) from that of Omicron -- which is the whole point...

On 2022-01-09 12:17:20, user Christopher Hickie wrote:

What? No comments allowed? I am a pediatrician with a PhD in neuroscience. My comment earlier was valid.

On 2022-01-07 10:51:52, user Zacharias Fögen wrote:

Dear Authors, <br /> your cohort is not well matched. You have +4.8% unvaccinated in Delta which are essentially replaced by 2x vaccinated. Considering the huge protection the vaccinated have for severe outcomes, this is clearly a bias. please use 1:1 matching.<br /> Also, since age is a very strong predictor, (about risk x2 per 6-7 years), please use 5 years age groups and also use it for people aged 80+ for matching purposes. <br /> if possible, also take a closer look at the risks of age groups 60+ by relinquishing region and onset date to increase the cohort.<br /> best,<br /> Zacharias Fögen

On 2022-01-07 12:28:26, user Alex Frost wrote:

So...robust evidence of strong protection via prior infection (lower for Omicron but still c. 60%).<br /> Separately, clear evidence of protection against symptomatic infection against Omicron for 3 doses of vaccine (2 shots + booster). <br /> Has anyone studied the effect of hybrid immunity = prior infection + 3 doses/3 doses + breakthrough infection? Surely that is endgame for global populations against Covid19.

On 2022-01-10 09:28:33, user RBNZ wrote:

How can there be 83 covid related events in the unvaccinated population (n=11)? 3 of the unvaccinated had "No chronic disease", does that mean that 8 had chronic disease? This would be a significant confounder due the small size of the unvaccinated.

On 2022-01-10 14:20:30, user Siguna Mueller, PhD, PhD wrote:

Dear authors,

thank you for your detailed results. Regarding the stats: an average patient just does not exist. Your SDs are rather big. Can you possibly say anything about characteristics of those individuals that exhibited negative efficacy? Is there any overlap to those groups that were excluded during the initial trials?

Thank you.

On 2022-01-18 15:16:11, user Dena Schanzer wrote:

Dear Authors:

I suggest looking at the historic trends in the rate ratio, or the relative risk (RR) of testing positive for COVID-19 for vaccinated compared to unvaccinated Ontario populations. The crude rate ratio can be calculated daily for cases, hospital and ICU occupancy from population level data provided by Ontario Public Health (https://data.ontario.ca/en/dataset/covid-19-vaccine-data-in-ontario ). The crude RR dropped below 1 by the end of December 2021 and has since steadied around 0.8 since Ontario closed high risk venues such as bars and restaurants. Hence, as this study suggests, it seems quite clear that the vaccinated population as a whole is currently at higher risk of infection than those who are unvaccinated. And, it is not surprising that a VE calculated as 1-OR, even in the test-negative control design would eventually become negative as well.

The steady decline in the crude RR can likely be explained by the lack of mixing between the vaccinated and unvaccinated populations (accentuated by the vaccine passport) and the higher transmission rate in the vaccinated group. If the effective reproductive number (Re) is higher in the vaccinated group, the RR should continue to decline even if the VE is held constant. It would be very helpful get your infectious disease colleagues (from the Ontario Science Table) to run a few infectious disease model simulations. I suspect that differences in Re were responsible for some of the downward drift in the RR in November when delta dominated. I’d suggest including the control group in the modelling exercise as well. I doubt that the Re gap is the same in the ‘other respiratory virus’ group. If it is (for example if you use the double vaccinated as the control for triple vaccinated), I would expect your test-negative control design would effectively control for biases introduced by the drift in exposure risks.

This study raises interesting questions. In the end, we will have a better understanding of how to monitor epidemics in near real-time. Perhaps monitoring the difference in the week-over-week percentage change in the vaccinated and unvaccinated groups could have provided an early warning indicator that either VE has dropped or contact rates have increased in the vaccinated group to a level where the vaccinated start driving the epidemic growth. Simulation studies should provide valuable insight on how to interpret this data!

Dena Schanzer

On 2022-01-13 14:05:52, user Zsofi Igloi wrote:

now published in: https://pubmed.ncbi.nlm.nih...

On 2022-01-13 14:39:26, user Peter wrote:

I thought this was a fascinating article. I tweeted.

I thought that the conclusion went further than the evidence.

You state that "…have<br /> been training dogs to detect Sars-CoV-2 virus in human sweat, by detecting volatile organic<br /> compounds (VOCs) in infected patients [1]. The VOCs exact nature is still under identification<br /> [2]."

In other words, you do not suggest that the dogs detect the virus per se; just that whatever they smell allows them to distinguish people with Covid-19 from people without the infection.

This study shows that they can detect the same smell in at least some people with Long Covid.

But you then conclude that "This study suggests the persistence of a viral infection in some Long COVID patients".

Given that there is nothing to suggest that the dogs can smell the virus, per se, the fact that they can detect the same smell in people with Long Covid certainly does not suggest the persistence of a viral infection.

There may be many hypotheses - probably better hypotheses - to explain the finding; but the conclusion is clearly unwarranted.

I do not know if you saw the tweet https://twitter.com/Evidenc... from @EvidenceMatters in reply to my tweets. It reads: "Beyond the information that none of the LC people had been admitted to ICU, it would have been helpful to know how many had been hospitalised and some info. about their vaccine history/plausible variant for infection etc.<br /> I was unclear on how many sniff sessions there had been."

I note that the paper has not yet been peer reviewed. Perhaps you will address some of these points before it is published.

On 2022-01-14 00:43:08, user disqus_mV149tuM7g wrote:

I am not a medical professional, but a common sense confounding variable immediately popped up in my mind, for which this (and most other studies) did not control for (though I understand it may not have been possible to control for it in this study given the data collection method, but more so I am baffled that from what I see 0 scientists and humans on earth apparently have thought of this common sense confounding variable and 0 studies that I know for attempted to control for it):

A) Do we not know that omicron is more similar to the common cold compare to delta? B) Do we not know that there is at least some common T cell protection across different coronaviruses, such that even T cells produced from a common cold give at least some protection against covid?

So then, without any further medical knowledge, the immediate common sense confounding variable that pops up in my mind using basic inferential logic is that if A and B are true, could it be that given the timing of omicron (came in early winter) compared to delta (came in summer), much more people had a common cold before omicron as opposed to delta? Also, less people abided by restrictions in Fall 2021 compared to Spring 2021. So couldn't this partially be the reason for why "omicron" is more mild than delta? Of course, that would mean that "omicron in those who had a common cold recently" is more mild than delta, NOT that "omicron" is more mild than delta. Do you see how dangerous it is (for people who did not have a common cold in a long time, especially if unvaccinated) to claim that "omicron" is more mild than delta? Again, I don't know if all of this is true or not, but I certainly think it warrants a more closer look.

Another confounding variable I can think of (though this one I am less certain of, but I don't think it hurts to put it out there): I remember early studies in 2020 showed viral load was associated with illness severity, and that those who wore masks tended to have less severe illness. Assuming those studies were correct, could it be that because omicron is more transmissible, more people are getting infected with omicron with low viral load compared to delta? For example, maybe more people are getting delta through droplet spread resulting in higher viral load, and more people who wear surgical masks but get omicron due to being in a small store with enough aerosols going through the mask and giving them omicron get omicron, resulting in less viral loads overall for omicron infections. Has this been controlled for? I have yet to see any studies that controlled for it.

On 2022-01-17 23:34:14, user Saar Wilf wrote:

Thank you for sharing this very interesting data! <br /> Unfortunately, I don't think it supports the suggested conclusion.

A few things don't match the hypothesis:<br /> 1. Hospitalizations don't show the pattern you'd expect under the hypothesis. There are more unvaccinated hospitalized on the day of PCR+, but after that there is no difference. <br /> 2. There seems to be no effect below age 55.

It's unlikely for a treatment to have an ongoing effect on deaths but not on hospitalizations, and only at certain age groups.

So what could it be?

I believe the hospitalizations on the day of PCR+ are a sign of either:<br /> 1. Hospitalization for another reason and PCR+ upon admission.<br /> 2. Hospitalization immediately upon PCR+ due to the patient being high risk.

I couldn't understand whether date of PCR means date of swabbing or date of result, That would determine which of the two is the correct interpretation (if any).

If it is 1, then I believe the entire finding is an artifact of unvaccinated older people being less likely to use (or have easy access to) health services. They are therefore likely to seek hospital care only in life threatening situations, and therefore more likely to die following hospitalization.

If it is 2, then I believe the entire finding is an artifact of very frail people not being vaccinated due to their state, hospitalized immediately upon PCR+, and then having a higher probability of death unrelated to vaccination status.

You are welcome to discuss further on twitter @saarwilf

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 2022-02-15 08:41:08, user Sylvia van der Woude wrote:

What a poor study with a far too small sample size!!! Was it cherry-picked from a much larger pool of patients? Also, symptoms were not taken into account, even though they are most important! ps: Isn't the funding by the B&M Gates foundation a conflict of interest?

On 2022-02-16 21:04:27, user Showme wrote:

How long is the peer review still going to take ... ?

On 2022-02-20 16:13:15, user pwlg wrote:

Reassuring evidence from South Africa indeed and hopefully similar results are seen in Denmark as BA.2 becomes dominant there.

On 2021-08-10 11:20:40, user Austen El-Osta wrote:

Dear Dr Stephen Gilbert,

Many thanks for your email, comments & for taking an interest in our paper.

We will address the concerned you raised in the updated manuscript when we receive feedback from reviewers (currently in process). I will briefly address your comments here but will address in full in future iterations of the manuscript.

Major concern 1 of bias towards study funder: The paper not only assesses the utility of a methodology, it also applies that methodology to report on relative performance of different symptom checkers (i.e. benchmarking). We did not intend for our results to be biased. Our decision to include some data from Ada & Babylon was to consider the suitability of vignettes in ‘benchmarking’ the performance of any online symptom checker. The reason for the smaller number of tests (& utilising a smaller number of inputters) was purely for reasons of pragmatism as the work involved in ‘inputting’ was very tedious/laborious. The benchmarking was not to determine which OSC is ‘better’ but to consider the suitability of utilising vignettes for this purpose.

Major concern 2 of bias towards study funder: There is also an important bias in selecting the results in the abstract. We can update the abstract to include the relevant data for all 3 OSC. The rationale for including the outcomes from Healthily was based on (1) significantly larger number of consultations, and (2) remain within the word count/limit.

We are committed to publishing a scholarly paper to iteratively advance knowledge in this space. The funder had no say in how we progressed the analysis or interpretation of the results. Thanks again for your email & feedback. I would be pleased to meet with you in future & to discuss the implications of our paper once it is published.

Kind regards,<br /> Austen<br /> 0777 288 2958

Dr Austen El-Osta<br /> Director- Self-Care Academic Research Unit (SCARU) – Department of Primary Care & Public Health- Imperial College London <br /> Primary Care Research Manager - School of Public Health- Imperial College London <br /> General Manager - Directorate of Public Health & Primary Care- Imperial College Healthcare NHS Trust<br /> 323 Reynolds Building | Charing Cross Hospital | London W68RF<br /> ====================================================<br /> T: +44 (0)20 7 594 7604 | M: 0777 288 2958 |E: a.el-osta@imperial.ac.uk<br /> P: http://www.imperial.ac.uk/p...<br /> W: https://www.imperial.ac.uk/...<br /> Twitter: @austenelosta @ImperialSCARU

On 2021-08-06 07:17:04, user disqus_UQJEvw3dWd wrote:

Dear Dr Austen El-Osta,

We read with interest this preprint article “What is the suitability of clinical vignettes in benchmarking the performance of online symptom checkers? An audit study”. Studies addressing the suitability of different evaluation methods are useful, and vignettes methods in particular have known advantages as well as known shortcomings (Fraser et al., 2018; Jungmann et al., 2019). Further detailed analysis into the overall utility of vignettes methodologies is certainly important. Whilst the approach taken for exploring vignette methodologies here is interesting and warrants reading and careful consideration, two aspects of the study conduct and reporting are deeply worrying.

We ask for the authors to correct aspects of the paper where there is unequal and unbalanced methodology applied to the funder symptom checker (Healthily), as compared to those applied to the symptom checkers of the funder’s competitors (Ada and Babylon).

We also ask that the authors report results in a balanced manner in the abstract. All outcome measures should be reported fairly, irrespective of whether the funder’s symptom checker performed well in any particular measure. Please see below for a detailed description of these aspects.

We do not state that the selective application of methodology and the selective reporting of results has been deliberately conducted to bias the study to the benefit of the funder. However, the degree of different treatment of the funder’s symptom checker is so large, that an independent reader could draw that conclusion. We suggest rectifying the highlighted issues in the preprint, and, before submitting the manuscript for peer review.

Should these issues not be addressed in any future peer review process, we will in due course, also write to the editor of the publishing peer review journal.

Major concern 1 of bias towards study funder: The paper not only assesses the utility of a methodology, it also applies that methodology to report on relative performance of different symptom checkers (i.e. benchmarking).

This approach would be fair if the same methodology were applied to all the symptom checkers, however, the study presents a grossly unmatched analysis. One approach has been used for the funder’s symptom checker (Healthily) and a second for the symptom checkers of two main competitors of the funder. This gives the appearance of fundamental bias in testing and reporting based on study funding. Although some degree of bias may be introduced in studies for a multitude of reasons, deliberate application of fundamentally different testing methodologies to the products of the funder compared to those applied for their competitors is unacceptable. The Healthily symptom checker was tested with 6 inputters (4 professional non-doctor & 2 lay), whilst, for no rational justification, the Ada and Babylon symptom checker were tested with a testing group of fundamentally different make-up (not just the number of testers, but a systematic and deliberate choice to use a different type of tester population, i,e. 4 professional non-doctor inputters).

The number of tests also differed greatly (n=816 for Healthily, vs n=272 for Ada and Babylon). Additionally, only one professional non-doctor inputter recorded the consultation outcome and triage recommendation using Ada and Babylon symptom checkers, for all 139 vignettes, which is in contrast to the approach the authors adopted for Healthily.

Major concern 2 of bias towards study funder: There is also an important bias in selecting the results in the abstract. <br /> With respect to condition suggestion: In the results section, it is reported that “Ada consistently performed better than Healthily and Babylon in providing the correct consultation outcome in D1, D2 and D3” (i.e. in the provision of correct condition suggestions). The difference in performance was large: “The correct consultation outcome for Ada against the RCGP Standard at any disposition was 54.0% compared to 37.4% for Healthily and 28.1% for Babylon”. It is acknowledged in the abstract that condition suggestion (referred to as disposition/diagnosis) is a main outcome measure, however this measure is not reported in the abstract. This looks like selective reporting in the abstract to avoid negative messages about the funder’s symptom checker.

With respect to ‘triage recommendation’:<br /> It is reported in the results that “In benchmarking against the original RCGP standard, Healthily provided an appropriate triage recommendation 43.3% (95% CI 39.2%, 47.6%) of the time, whereas Ada and Babylon were correct 61.2% (95% CI 52.5%, 69.3%) and 57.6%, (95% CI 48.9%, 65.9%) of the time respectively (p<0.001). Again, this is omitted from the abstract, where only the aspects of the relatively positive performance of the funder’s symptom checker are reported.

We would welcome a change in this study to remove bias towards the funder in methodology and results reporting.

Yours faithfully

On behalf of Ada Health GmbH<br /> Dr. Stephen Gilbert<br /> Clinical Evaluation Director<br /> Ada Health GmbH<br /> Karl-Liebknecht-Str. 1<br /> 10178 Berlin, DE <br /> +49 (0) 152 0713 0836

REFERENCES

Fraser, H., Coiera, E., Wong, D., 2018. Safety of patient-facing digital symptom checkers. The Lancet 392, 2263–2264. https://doi.org/10.1016/S01...

Jungmann, S.M., Klan, T., Kuhn, S., Jungmann, F., 2019. Accuracy of a Chatbot (Ada) in the Diagnosis of Mental Disorders: Comparative Case Study With Lay and Expert Users. JMIR Formative Research 3, e13863. https://doi.org/10.2196/13863

On 2021-08-19 19:40:41, user J.A. wrote:

Three comments: <br /> 1) Table 1 is highly confusing, and the explanation does not make any sense. In re-reading the original NEJM clinical trial appendix, the explanation is very clear. Here it is not. The time from exposure to starting HCQ does not match the public data set. The authors here have changed the data to make it look longer than it is in Tables 1 and Tables 2. The altered / falsified data are obvious when looking at the public dataset as no one had a delay from exposure to starting study drug of 7 days. Perhaps the authors don't understand the public dataset or are they altering data?

2) As per prior comment in version 2, this post-hoc analysis appears to be driven by an artifacts of differing event rates in the subgroups of the placebo versus intervention group. The authors have not recognized this nor commented upon this. The authors should create a graph of time from exposure by day vs. covid-19 incidence. The artifact is visually obvious.

3) This is a faulty analysis which is typical for a post-hoc analysis. It does not follow published best practices on subgroup analyses. Post-hoc analyses are generally hypothesis generating and require validation in future studies. In this case two separate clinical trials did not replicate any of the findings presented in this pre-print.

On 2021-04-27 22:06:58, user Tom Argoaic wrote:

I've looked over the public data set released by the Minnesota group, plus their later publication about their studies, and I can't figure out how to correlate the shipping times you used in this paper with their data set. Did you alter or adjust the shipping times in your paper? And if so, how? I didn't see any description of this in your methods, which makes me wonder where you came up with your numbers as I try to replicate the data you presented here.

Data sets I used, sent by their team:<br /> https://drive.google.com/dr...<br /> https://drive.google.com/dr...

Their paper that goes over their protocol and shipping times:<br /> https://academic.oup.com/of...

On 2021-07-04 15:46:32, user AF wrote:

The authors fail to describe the online questionnaire - did it restrict the set of symptoms that were able to be reported? Did it allow entry of non-predefined symptoms? Did it include a severity scale for each reported symptom? The precise questioning schema might have a very significant effect on the results which seem obviously in contradiction to e.g. UK ONS data.

On 2021-07-07 18:10:04, user rusbowden wrote:

Research abounds that shows masks work for #COVID19. Should we use them for the flu too?

Yes, shows this study: https://www.medrxiv.org/con...

from the abstract: "Particularly, our simulations suggest that a minority of individuals wearing masks greatly reduce the number of influenza infections. Considering the efficacy rates of masks and the relatively insignificant monetary cost, we highlight that it may be a viable alternative or complement to influenza vaccinations."

On 2021-05-27 02:28:39, user Stel-1776 wrote:

It did not look at the effectiveness of masks, but the effectiveness of mask MANDATES. It should read "Mask MANDATES did not slow the spread". Why? Too many people who think they know better than professionals who dedicate their lives to studying this field. Too many people not wearing them, wearing them incorrectly, wearing the wrong type, not cleaning them, etc.

N95 masks are better, but there is solid evidence that regular surgical masks also reduce chance of spreading in the community.

This is supported by a systematic review (a review and critique of published studies to date) published in one of the most highly respected medical journals in the world.

"The authors identified 172 observational coronavirus studies across 16 countries; 38 of these studies specifically studied face masks and the risk of COVID-19 illness. The authors found that the use of either an N95 respirator or face mask (e.g., disposable surgical masks or similar reusable 12–16-layer cotton masks) by those exposed to infected individuals was associated with a large reduction in risk of infection (up to an 85% reduced risk). The use of face masks was protective for both health-care workers and people in the community exposed to infection."<br /> [Chu et al. COVID-19 Systematic Urgent Review Group Effort (SURGE) study authors. Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis. Lancet. 2020]

On 2021-07-10 10:07:37, user Caio Salvino wrote:

Hi.<br /> In my opinion, it’s impossible affirm that the “assymptomatic” cases was transmiting the virus without verify the cycle threshold of RT-PCR detecting RNA at oropharyngeal swabs samples. Only reporting like positive or negative is insuficient for affirm infectivity.

On 2021-07-10 13:39:38, user Marco Roccetti wrote:

Final version of this study available on BMJ Open: https://bmjopen.bmj.com/con...

regards<br /> The Authors

On 2021-07-12 18:04:47, user Robert Eibl wrote:

The abstract and the PDF manuscript clearly mention the 25 microgram per dose; it is known to those interested in the field that the doses used for vaccination are 100 microgram, but it really should be mentioned.

On 2021-07-13 19:11:26, user StephenWV wrote:

Stephen Smith suggested this study could be used by other studies for evaluation. How does it mesh with this study of Hydroxychloroquine alone and hydroxychloroquine with azithromycin when receiving hydroxychloroquine within the first 24 hours of admission.<br /> Treatment with hydroxychloroquine, azithromycin, and combination in patients hospitalized with COVID-19 - International Journal of Infectious Diseases (ijidonline.com) https://www.ijidonline.com/... <br /> This shows a reduction in morbidity of 290%

On 2021-07-18 15:08:21, user Larry James wrote:

Many physicians in the US are unfamiliar with using HCQ (HYDROXYCHLOROQUIN), since it is not a commonly prescribed medication. I am a physician in this category. In the very beginning of the pandemic, I was curious so I researched HCQ and found it listed as a very low risk QTC prolonging medication (it was in the same risk category as high dose Celexa (citalopram). (QTC is a heart EKG measurement). HCQ can be bought inexpensively without a prescription in many countries and it has been in use for over 30 years. Today, if you look up HCQ on QTC risk medication websites , you will see that it has jumped up two categories of risk, to the highest risk category known for prolonging QTC. This has likely had a chilling impact on its use. One must really consider the rational and legitimacy for this new QTC risk determination, particularly in light of its long history of safe use, its potential benefit an illness which had no other real medication treatment options and the fact that it is a very inexpensive medication. Of further interests is the fact that a fraudulent article was published through an esteemed publication, Lancet, during the beginning of the pandemic, which almost instantly shut down studies that were ongoing at that time.

On 2021-06-10 09:22:10, user cat's eyes wrote:

What were the baseline characteristics of the 37 patients who survived on HCQ compared to the patients who died? From Table 1 patients who survived were generally healthier and younger than those who died. Table 4 should provide adjusted and unadjusted hazard ratios. Also, did you test for interactions between HCQ/AZM and predictors such as age and steroid use?

On 2021-07-14 13:52:21, user djconnel wrote:

Cases in San Francisco have gone from a 7-day average of 10, to over 70 in just 20 days, with positivity rates from 0.5% up to 3.2%, which for a serial interval of 5 days, corresponds to an R of exp(5*ln(6.4)/20) = 1.6 (using positivity). Your algorithm needs to be tuned if it's yielding < 1.1. Plot of rolling 7-day case totals.

On 2021-07-16 09:03:00, user Ashish Agrawal wrote:

I am myself fully vaccinated with Covaxin with no side effects and have an IGG antibody score of 150.00 which is apparently enough as per many studies. <br /> While vaccinating with Inactivated virus vaccines one should maintain proper isolation from those who're not vaccinated with it. I don't think travelling between the dose 1 and dose 2+ 14 days was a good idea. Vaccines work but they take time in changing environment

On 2021-07-19 10:59:49, user Kaja Abbas wrote:

This paper was published in eLIfe on June 24, 2021.<br /> https://doi.org/10.7554/eLife.67023

On 2021-07-19 15:32:51, user Marco Pellegrini wrote:

This paper in revised form has been accepted in a peer-reviewed journal:<br /> Pellegrini, <br /> M. Accurate prediction of breast cancer survival through coherent voting networks with gene expression profiling.<br /> Sci Rep 11, 14645 (2021). https://doi.org/10.1038/s41...<br /> link: https://www.nature.com/arti...

On 2021-07-21 19:03:10, user Penny Butler wrote:

Comments: #82 (but I only see 8) ummm? #pleaseexplain

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 2021-07-29 05:43:08, user FUnlim wrote:

Even though the authors claim to have desmostrated the downstream mechanisms by which the infection inpairs neuronal viability, mechanistically the manuscript still remains lacking of support for that.

Despite the fact that conditioned medium of SARS-CoV-2-infected astrocytes reduces neuronal viability, this can be caused by many things beside the metabolic alterations. So, it should be tested.

All conclusions are based on proteomics data only. They should validate the metabolite levels, mainly for the highlighted ones.

On 2021-07-29 08:16:26, user Enzo wrote:

Comparing the rates of severe adverse events such as VTE or TCP between groups of vaccinated people and groups of Covid-19 patients is not likely to be a sufficient way to evaluate risk/benefit ratio. One should take into account that the number of people who get Covid in a year is many-fold smaller than the nummber of people receiving a vaccine jab. (Approx 200 million people got Covid in the world in 20 months, vs 2 billion people who received at least 1 dose, and 4 billion doses already received in 8 months.)<br /> So, even with a 15-fold higher rate of excess VTE/TCP among covid-19 patients than among vaccinated people, if the number of vaccinated people (or jabs) is more than 15-fold higher than the number of covid-19 infections during a given period of time, then vaccination campaigns are to produce more VTE/TCP victims than Covid-19.<br /> ("number of vaccinated people (or jabs)" because if the increased risk linked to vaccines is specific to not yet identified "at risk persons", the number of vaccinated people should be taken into account. If it's inherent to each injection, the number of doses should be taken into account.)

On 2021-08-01 15:04:13, user VirusWar wrote:

Hello, I've got serious doubts about this study :<br /> * performing RT-PCR tests up to 50 Cycle Threshold is not reliable. For exemple virus was found in Caravage (Michelangelo Merisi) body dead in 1610 with 45 CT !<br /> * vaccinated people were on one site and different testing methods were used on each site, so there is a systematic bias<br /> * for plenty of patients, included vaccinated ones, the study reports viral load up to 4 weeks. This is not emphasis in the text but it is quite extraordinary according to what we know currently. Details of viral load data and screening methods should be shared to check this

On 2021-08-02 18:48:48, user Sam Stampfer wrote:

Interesting & alarming paper.

I emailed the authors regarding this, but thought I'd also put this as a comment:<br /> Figure 3: comparative neutralization responses between variants & wild-type. The serum was drawn at a median of 6 days post breakthrough infection. This isn't enough time for new delta-variant-specific antibodies to form and thus probably reflects the anamnestic memory-b-cell response from original vaccination. I was wondering whether the authors have followed up and evaluated more convalescent sera >2 weeks post-symptoms to see whether it is better able to neutralize delta. It is especially telling to me that the delta neutralization was even worse appearing than the beta neutralization, which would be unexpected unless there was no delta-variant-specific response.

On 2021-08-03 00:44:35, user practiCalfMRI wrote:

Any chance you could assess hematocrit levels in the COVID group before/after? While I wouldn't expect any significant changes in Hct for the controls, the WBC count for the post-infection group could be significantly increased, with RBC count decreased accordingly. (Pretty standard post-viral infection effect, esp. w/ serious disease.) If Hct is indeed lower, I would then wonder if there might have been a compensatory boost in local CBV which, by virtue of the different blood and tissue T1s, happens to manifest as patchy changes in apparent GM volume.

On 2021-08-03 02:27:45, user Kathy Cardy Thompson wrote:

My daughter has a genetic disorder SLC30A9, she is Australian, and comes from 2 parents not related. Has had this fault for years now. There are also kids in England and to date approx 14 kids in total world wide. If you search you will see they are of mixed cultures, so not predominantly African American. My daughter has dealt with issues from SLC30A9 gene now and it’s destroying her life, America also were contacted and knew about my daughter before this report was published.

On 2021-08-05 10:30:00, user Piotr Goryl wrote:

Dear Authors,

You have compared 62 samples of Delta variant with 63 of 19A/19B to estimate relative viral load of two variants. How the samples were chosen? When this 63 of 19A/19B samples where collected?

All the best,<br /> Piotr

On 2021-08-08 08:57:23, user Raman wrote:

I am really glad that this study has been done; though the numbers are relatively small, I am glad that it has been executed well. As a vaccinologist, I have been feeling strongly that a heterologous prime-boost of covaxin followed by covishield (In my opinion, this would have been a more logical approach than the one tried here) would give better results than either of the vaccines in a homologous prime-boost mode. Glad that this itself has given good results. In case, we go for a third dose, we should perhaps switch over, though the logistics of such an approach would be difficult if not impossible).<br /> V.D.Ramanathan MBBS, PhD (London), <br /> Scientist G (Retd),<br /> National Institute for Research in Tuberculosis (ICMR),<br /> vdrnathan@gmail.com<br /> 8 Aug 2021

On 2021-05-16 20:45:44, user vinu arumugham wrote:

Vaccine content can vary widely. So to understand the relevance of your observed effects, we need to know where the studied product characteristics fall compared to the entire space of product characteristics.<br /> https://www.regulations.gov...<br /> www.sciencedirect.com/scien...

Further, T cells induced by injected vaccines home to the skin. T cells induced by natural infection, home to the lungs.

On 2021-05-21 22:52:28, user Ilmari Haavisto wrote:

The beginning state of medication of disease is very important. Was it equal to both groups?