3 ms·
I happen to know a lot of doctors, including, as an example, an OBGYN. As it was explained to me, for vaginal births: - at some point someone, without evidence
by idoh 5y ago
I happen to know a lot of doctors, including, as an example, an OBGYN. As it was explained to me, for vaginal births:
- at some point someone, without evidence, speculated that cervix dilation should proceed along some curve
- cervix dilation is actually measured by hand - literally inserting fingers and having the doctor practice "so many fingers = so many centimeters". There's plastic sheets with holes in them so they can practice measuring the size of holes with fingers.
- the OBGYN knows that the cervix dilation curve should look like, and kinda sorta maps their hand readings to what it should look like
- the OBGYN has a general sense as to how labor is going, and will game the cervix dilation stats to match their expectation, e.g. if labor is going well but the cervix hasn't dilated then they'll kinda sorta report progress anyway
Anyway, given the above it seems like the data around cervix dilation is suspect - the measurements are fitted to what the curve should look like, and then the data matches the curve, and that makes people more confident in the curve, and so on.
The point is, can you really apply ML to the EMR of cervix dilation? Does it make sense, could you really draw conclusions from this?
- pc86 5y agoJust to clarify, this OB will report incorrect clinical data to support how they feel labor is going?
- idoh 5y agoIn some situations yes, in others, if they read a 5.5 or a 6, then they will pick the one that fits.
- calvano915 5y agoIn cases of subjective data, you will always see variances in reporting that may be construed as misrepresentation. It's not at all easy. As "objective" as the example might seem, I'd argue the clinician has too much leeway to truly be objective. There's a whole ton of subjective data involved in every patient course of care, much more than objective in many cases.
- YeGoblynQueenne 5y ago>> The point is, can you really apply ML to the EMR of cervix dilation? Yes, it's a perfect fit. If I may. Sorry, to clarify, the way most people do machine learning is what you describe: tweak a model until it fits the dataset. If the ability of the model to fit the dataset translates to anything beyond that, it's anybody's guess. You just put me off being a parent for life, btw.