I think the problem here is that most people who do epidemiological research - including many who might call themselves 'epidemiologists' - have not actually had any epidemiology training. I wish it was a protected title. With an exam that would require proving you understand residual confounding!

Otherwise, I'm more considered about pre-validation issues — correcting for chronological age without nested cross-validation in biomarker prediction models.

The nonlinear thing becomes an issue if you want to make a claim that residuals are independent of age.

For example, current evidence is that LLMs rely too much on interpolation rather than discovering underlying invariant causal abstractions.

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Same goes for structure determines function concepts

I think Cox is earnest. It is scary to listen to him because he is right that the AI presentation of "facts" is largely based on what is popular, but you can also steer it to selectively give you evidence that confirms your own worldview.

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What year was this picture taken in?

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The grass is not greener, I'm afraid.

It's a niche for sure. But it exists and it's underutilized and could be further developed

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She wrote this in May…

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I also think most of the authors were not solid neuroimaging researchers. It was like a fun project. Not particularly good science.

substack.com/@manjarinara...

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