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This study builds AI models that score diagnostic screening tests, and that predict screening results, clinical judgment, and life expectancy. Longevity Metrics collects a battery of clinical tests on each participant, in whole or in part, and follows every participant for life. The sit-to-rise test and the timed walk are scored by hand today, from a person's count. A model scores the same test from video instead. It also measures what no one can count by eye - speed, asymmetry, steadiness - so one capture yields both the original score and additional measurements, intended to enrich the model and strengthen what it predicts. Every test in a participant's record measures the same body, so the tests are correlated: a test that was performed carries information about one that was not. A model trained across the library learns those relationships and estimates a missing result from the re
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