4 ms·

Then you should fund it. The entire field is to my understanding absolutely starved of science funding. There are two fairly strong clusters of findings that a

• timr 4mo ago
  All of those are examples of exactly what I told you about: they take a group of people claiming to be sick, and go hunting for signals to claim as “significant”. The MRI studies are particularly egregious examples of this. Just because you see a difference on an MRI does not mean that the difference is due to the thing you’re blaming. In fact, it almost never is. > If your argument is that people are showing up with abnormalities, then diagnosed with Long Covid, then spurious biomarkers are associated to it - you are just wrong. Wrong multiple times. Demonstrably so. I am? I have now followed every link. Literally every paper you posted is following this exact pattern. I don't know how you could possibly conclude otherwise, unless you just didn't read past the titles. They each take a (typically small) cohort of people who self-identify as "long covid sufferers", they subject them to random combinations of tests, and report only what they find to be significant. It's literally the XKCD comic about jelly beans. https://xkcd.com/882/ https://xkcd.com/882/
  • kranke155 4mo ago
    You are just ignoring the evidence, being unscientific, and unless you work for a top medical lab somewhere, plain arrogant. The UK Biobank study scanned participants before and after infection with matched controls. The difference is measured against their own pre-infection brain. That is the opposite of what you're describing.
    • timr 4mo ago
      > You are just ignoring the evidence, being unscientific, and unless you work for a top medical lab somewhere, plain arrogant. If you don't know how to interpret evidence, then I suppose it would sound like I am being overly critical. I didn't bother to pick on just one, but since you chose it [1]... > The UK Biobank study scanned participants before and after infection with matched controls. The difference is measured against their own pre-infection brain. That is the opposite of what you're describing. It is not. The longitudinal nature of the study is a distraction from the fundamental issues with the approach. They did a longitudinal case-control study, one group of which had positive covid tests in the past, and the other one did not at the time of the second scan (2021). That's the entire evidence base that this study is built upon -- it has nothing to do with "long Covid", and it's only barely plausible that the control group is actually a control for the factors of interest. Next, they took two scans for all participants - one from before the pandemic, and one made after (again, in 2021). They made over 6000 different images, and then cherry-picked the ones with differences for further analysis (~70). Ultimately only 6 of these fishing expeditions survived family-wise error correction: > The main case-versus-control analysis between the 401 SARS-CoV-2 cases and 384 controls (Model 1) on 297 olfactory-related cerebral IDPs yielded 68 significant results after FDR correction for multiple comparisons, including 6 that survived FWE correction So first off, no statistical correction can compensate for this fundamental bias. You cannot start with thousands of different samples - even if they're taken from the same people at different time points - and winnow that down to a handful by filtering on the outcome of interest, Applying a multiple-sample correction will not fix it. It's not even clear that there is such a correction that is valid for the underlying distribution of the data involved. But setting that aside, the differences observed, even between longitudinal samples, do not have to be due to Covid! Even if they're not random (which we cannot grant; see previous paragraph) they could be due to everyone being locked inside during 2020. They could be due to factors completely unexamined by the study, like, say, increases in drinking or drug use, or lack of exercise. Or any of a million other things. We don't know. The authors don't know. They're just not intellectually honest enough to admit that they don't know. I could go on, and point out more flaws (e.g. the "significant" results mostly disappear when you exclude hospitalized patients, yet oddly, the difference between "hostipitalized" and "control" cohorts is not itself significant, indicating inadequate statistics), but this post is already too long. I'm sorry that you think this is arrogant, but this is how we actually read papers. [1] https://pmc.ncbi.nlm.nih.gov/articles/PMC9046077 https://pmc.ncbi.nlm.nih.gov/articles/PMC9046077