24/n None of this should matter, because the trial found NO BENEFIT FOR IVERMECTIN, but this has been reported and included into ivmmeta dot com as a hugely beneficial resultpic.twitter.com/mjGBDnpVW5
Epidemiologist. Writer (Guardian, Observer etc). "Well known research trouble-maker". PhDing at @UoW Host of @senscipod Email gidmk.healthnerd@gmail.com he/him
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24/n None of this should matter, because the trial found NO BENEFIT FOR IVERMECTIN, but this has been reported and included into ivmmeta dot com as a hugely beneficial resultpic.twitter.com/mjGBDnpVW5
25/n This explains the bias I noted above - it's not publication bias, it's that the authors appear to have generally chosen whichever result makes ivermectin look better to include in their model Not really scientific, that!
26/n But the fun doesn't stop there. The inclusion criteria for this website is any study published on ivermectin, which has led to what I can only call total junk science being lumped in with decent studies
27/n Here's a study with impossible percentages in table 1 that used a comparator of 12 completely random patients as their control. They don't even say if these 12 people had COVID-19 Included in ivmmeta, no questions askedpic.twitter.com/3AlGdGcPnW
Health Nerd is correct that the percentages cannot be the result of the numbers listed. But he's there is a lot of context that should be understood, and I find this in fully half the papers I read, regardless of other qualities. It happens when...
It happens most often when the staff is small and not well enough trained in number sense to catch where they've changed the underlying patient pool after computing the percentages. Once in a blue moon, such an error is entirely critical to the outcome. Mostly, it's messy.
Typically, in nations outside the Western pharmasphere, this happens because it's mostly doctors working without a professional statistician, and that does not mean the labor itself was not quality.
Lol, that's absolute nonsense. Some of the best research during COVID-19 has come from outside the "Western pharmasphere", most of it includes statisticians and does not have impossible values in the studies
I'm sure we can find some of the best papers that have a larger staff and can find all the errors. That's not the point. The point is that saying, "somebody made a number mistake, so all this is invalid" is a poor critique. Just ask for their data, and suggest a correction.
Normally, impossible values throughout a paper is cause for some concern about whether a study has been done correctly. There are also quite a few other concerns with this particular piece of research 
Indeed, this author has refused to share data until "after the pandemic" when we asked to check for potential fraud, and the pre registration of this study actually contradicts the publication directly! It's extraordinarily low-quality research
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