Is the study fraudulent? Could be, and his fight to use Ivermectin on Covid patients always struck me as quackery as well. That said, it seems to me that there is something about the Vitamin C protocol that should not be dismissed.
And this is exactly why scientists rightfully ignore "single data points" such as this. People get sick and recover all the time. And sometimes people get very, very, very sick, and they still recover on their own.
So there would be a non-trivial percentage of people who would have gotten very sick, and just based on timing it wouldn't be unusual that they would be given some treatment and then credit that treatment when they recovered.
Not only is this study blatant fraud, nine other studies tried to replicate the results and showed no benefit to sepsis outcomes.
The same applies for quite a wide range of other nutrients as well.
And as a statistician I can tell you with absolute certainty this paper is fraudulent.
Apparently this test is how likely it is for the same number of people to have the primary diagnosis in each group because all the 1.0 have equal number of patients with that diagnosis or off by 1. Like if they said, we have 8 people with COPD in the first group and then waited for COPD patients to show up at the ER for the second group until they had 8 then you would get a perfect 1.0 match since they made that variable the same? And then the other variables would be randomish, but maybe correlated so more than 0.5?
Maybe you can explain how this layman take is wrong, but if all that's going on is they selected patients with the same diagnoses and didn't make that clear in the paper then I don't understand what the big deal is.
Did you not read this? I asked whether if they did this they would come up with the perfect 1 scores on those items. Whether they actually did or not is a separate matter.
What troubles me more than whether this study was faked or not is the certainty HN readers have that it was without being able to answer simple questions about why they believe that. Is this test just measuring the likelihood of each group having the same primary diagnosis? If they added people to the second group so they had the same number of a primary diagnosis, would that result in a 1.0 p value on this test?
These should not be difficult questions to answer for somebody certain that fraud occurred.
2. Patient subgroup counts matched perfectly not only on COPD, but on about a dozen other conditions. Difficulty of matching subgroup counts grows rapidly with number of dimensions. To match subgroup counts for Group A and Group B near-perfectly on a dozen different dimensions, there's no substantially easier method than just one-to-one perfect matching, which requires a very, very large number of patients. You have to take Patient A1, who has subset X1 of 12 different conditions, and find Patient B1 with that exact same subset. Then repeat, 47 times in this case.
It is already quite hard to find the person B1 who has the exact subset X1 of conditions that person A1 had. For example, if there are 12 conditions, each condition is present in half the people that come into your clinic, and the conditions are independent, you'll need to go through 2^12 = 4096 people, on average, to find another exact match. The conditions may be a bit correlated but this can only help you so much when you're talking about 12 different conditions.
To repeat that feat 47 times is very hard. You'd have to churn through 10s or 100s of thousands of sepsis patients to get your matching subset. This would require access to an enormous pool of sepsis patients and constant reporting of all the conditions they have, that you want to match on. For this to be done without any mention in the paper is utterly beyond belief.
And the fact that the counts match near-perfectly in an off-by-1 fashion does not help the situation at all.
> Difficulty of matching subgroup counts grows rapidly with number of dimensions. ... and the conditions are independent, you'll need to go through 2^12 = 4096 people [for 12 conditions]
Except many of these are "primary" diagnoses, which presumably you have one of hence the name, so it's not possible for a person to have both a primary "Pneumonia" diagnosis and a primary "Other" diagnosis. So for each person maybe you're matching 2^3 or 2^4, not 2^12 - in any case, far less than your conclusions are based on.
> You'd have to churn through 10s or 100s of thousands of sepsis patients to get your matching subset.
So if the matching was 1/1000th as hard as you thought it was, that would be 10s or 100s of patients. A million cases a year, several doctors in major metro areas, there's probably 10s to 100s of patients at any given time in their hospital systems.
> Consecutive means you don't skip anyone who has the condition you're trying to treat.
Sepsis is very serious and common, so they'd have to treat them all basically simultaneously with the experimental treatment. I'm no expert, but I'd expect they'd want to be able to abort the trial if people started dying from it. It also doesn't even make sense as a study, because you want to test like for like as much as possible; maybe the treatment works fantastically on patients with cirrhosis and nobody else.
I think a plausible scenario is each day before normal rounds they did a med search for a patient matching the next one from first group, generally found a good match, worked with their doctor to change the treatment and added them to the study. Maybe two in a day, or skipping a day, and a month later they have really good matching data. Lots of cases to choose from, many exclusive variables. What do the statistics say for a charitable interpretation? Pretty good odds, right?
So maybe they didn't report their methods accurately, maybe it was assumed from domain knowledge or was a mistake when they cut and pasted from a template. Could be fraud too, but that seems like a huge leap to be certain of.
What you describe is selecting the group in advance so they match (though you use only one variable). There are cases where doing this selection is reasonable and is useful. However, the study describes a different selection process. If the selection process described was not the one actually used, what else in the study does not match reality?
If you trust anecdotes, your conclusions will be wrong most often than right. You might be lucky here and there, but will lose in the longrun.
Science is about being right most of the time and winning in the long-run.
However people get more than a bit overboard and woo-woo about Vitamin C.
Linus Pauling was a chemist and was obsessed with it but at least he was scientific about his quest.
https://en.wikipedia.org/wiki/Linus_Pauling#Medical_research...
and it gave us the Linus Pauling Institute which is a great thing for factual nutrition, I use it often as a reference:
I have never heard of this practice, and can find no evidence of it online. Can you provide a citation?
> If you consume vitamin C via citrus fruits like oranges, then you are making a more hostile environment for bacterial infections.
This is utter nonsense. The pH of a living organism is tightly regulated, and a failure of this regulation can be rapidly fatal. Consuming small (sub-gram) quantities of vitamin C does not have any meaningful effect on blood or extracellular pH.
Translated for laypeople:
This is BS. The pH of your body is constant and your body has mechanisms to make sure it stays constant. If your pH changed as claimed, you’d die.