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Stroke risk prediction tools are meant to guide how doctors approach a potentially deadly condition, using factors like heart disease and high blood pressure to get a handle on which patients might benefit from a particular treatment.

For years, doctors have used several different algorithms to try to capture the true risk of stroke, including newer models that use machine learning. A new analysis, led by researchers at Duke University School of Medicine, compared several of those algorithms head-to-head  — and found that novel machine learning models weren’t much more accurate at predicting the risk of stroke than  simpler algorithms based on self-reported risk factors and an older methodology. Alarmingly, the study also found all the algorithms were worse at stratifying risk for Black men and women than for white.

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