Discrimination remained stable across sexes; only calibration shifted in extreme scenarios when prevalence differed by sex, with similar patterns for women and men.

Using ~165k ECGs, we simulated sex-imbalances in representation (women-to-men ratio), outcome prevalence, and misclassification in the training data for LBBB, long QT syndrome, LVH, and physician-labeled “abnormal” ECGs.

Pre-print alert:
Many ECG-AI models have been developed to predict a wide range of cardiovascular outcomes. But, underrepresentation of women in cardiovascular studies raises the question: Are ECG-AI models equally predictive for women and men with sex-imbalanced training data?

ask chatGPT o3 this before submitting your next paper to, I got ~10 usable comments out of it:

you're a reviewer for <journal>; review the attached paper when you're either:

Liking this interaction with @mmbronstein.bsky.social and Denis Danilov so much I'm reposting it here

Can someone make a "science-not-politics" starter pack?

Great respect for academics who are politically engaged, and yes, bsky/X are places for political discussion / news sharing.

But science and politics take different parts of my brain; can we get science without political distractions?

Look at @maartenvsmeden.bsky.social go, he's won the VIDI grant! (arguably the most prestigious personal grant in the Netherlands at his career stage). Congrats!

Joost van Rosmalen from #umcutrecht kicking of the Causal Inference for AI meetup with his talk on using data from historical controls for trials with dynamic borrowing methods