Jay Bhattacharya

then therefore it must be psychological.

And so patients leave the doctor thinking the doctor thinks they're crazy.

It's going to be heterogeneous.

There's no one answer to that because the kinds of diseases or conditions we're talking about are so varied.

Even chronic Lyme is a good example of this.

There are patients who have had exactly the story you've told, and then there are patients who've had long bouts of antibiotics to try to rest and they still have the same chronic Lyme symptoms.

Same thing with autism.

Characterizing that is a scientific question.

A lot of the problems are that people have their sense of what works and what worked for them that doesn't necessarily generalize over.

I am really high on the ability of the scientific method applied honestly, where I don't think you're crazy just because you say you have a condition that I don't understand, to lead to improvements in treatment and prevention and things like that.

This is like autism.

Let's just be specific, right?

I worked very early on when I became an action director on this autism data science initiative.

And I explicitly in the call for proposal said, I don't know what the answer is.

I don't know what the ideology of autism is.

And so I want a wide range of hypotheses to be tested.

This is why I want to emphasize, I don't know the answer, and I want to let all of these hypotheses make their case using data.

Sure.

Okay, so this will come as a shock to folks who haven't heard about this, but it turns out that some chunk, maybe a large chunk, there's a lot of debate about exactly how much, of the published peer-reviewed scientific literature, even in top journals, when independent research teams look and try to answer the same question, do not find the same answer.

That is, a large chunk of the scientific literature is not reliable.