[cogman10]

But what if, for example, she's just sensitive to the smell of fecal matter and these people tend to have loser poops? Now is she detecting parkinson or is she detecting IBS or someone that ate something spicy or drank milk while being lactose intolerant.

The issue is the one of the false positive and bayesian statistics. If she's detecting something that has a bunch of common causes then it's not really helpful to run a suite of tests to find an underlying problem on everyone that smells the same.

A fever can be a sign of cancer, but it's also a sign of the flu. Should we check everyone with a fever for cancer?

[jondea]

I'm not an expert, but a very quick search showed a meta analysis[1] which considers the false positives of using volatile biomarkers as a diagnosis. The original paper[2], of which Joy is co-author has a much smaller sample size, but also has a control group to measure false positives.

Again, I'm not an expert, but from personal experience I know that Parkinson's can be hard to diagnose definitively until there are serious symptoms. This test may be relatively poor but still be useful as a piece of evidence.

[1]: https://www.sciencedirect.com/science/article/abs/pii/S03038...

[2]: https://pubs.acs.org/doi/full/10.1021/acscentsci.8b00879

[cogman10]

The science direct link isn't working for me.

But here's the problem as I see it. Parkinsons has an occurrence rate in the population of 0.1%. If there are conditions which cause the same smells as Parkinsons and they are more common in the population (1%, 5%, 10%) then this test all the sudden becomes very not useful because even at 1% occurrence rate in the population it's already 10x more likely that you have that condition rather than Parkinsons. That's the confounding problem. And a different comment here pointed out there are conditions that also seem to have exhibited the same smells.

Who knows, perhaps this is still worth it, but for an n=30 study, this is basically nothing to consider. The group size is simply way too small.

BTW, Medical media reporters really should have a "No reporting on studies with n < 500" rule. These sensational studies are always preliminary on really low population groups. I'd love to see the meta analysis to know how many studies it's lumped in and how big those are, though.

[jondea]

If it helps, the meta analysis is called: Volatile organic compounds analysis as promising biomarkers for Parkinson’s disease diagnosis: A systematic review and meta

Absolutely, it may not be useful as a screening test on the general population, but it may be useful as a piece of evidence for diagnosis alongside other pieces of evidence. Even for a test with a lot of false positives, the P(Parkinson's|positive) > P(Parkinson's|negative).

I generally agree that a lot of media doesn't accurately portray uncertainty in medical advancements, buts it's not as simple as having a sample size threshold. It really depends on the significance and the strength of the effect. Also, I want to hear about the exciting preliminary stuff, provided that it's properly caveated. There's just a lot of incentives to sensationalise.