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Clinical prediction model evaluation methods compared in new paper

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This paper compares two methods for evaluating clinical prediction models: decision curve analysis and cost curves. The authors demonstrate a close relationship between decision curves and Brier curves, a specific type of cost curve. Both approaches require users to set classification thresholds based on calibrated model scores and consider the relative importance of correct classifications. While net benefit and Brier loss will identify the same optimal model at a given threshold, they cannot be directly compared or aggregated across thresholds without custom weightings or specific assumptions. AI

RANK_REASON Academic paper published on arXiv detailing a comparison of evaluation methods for clinical prediction models. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.LG →

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Clinical prediction model evaluation methods compared in new paper

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Academic paper published on arXiv detailing a comparison of evaluation methods for clinical prediction models. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Louise AC Millard, Matthew Sperrin, Richard M Martin, Peter A Flach ·

    Evaluating utility of clinical prediction models: A comparison of decision curve analysis and cost curves

    arXiv:2509.24608v2 Announce Type: replace Abstract: Classification models typically predict a binary outcome and use one or more decision thresholds to produce a classification or inform a clinical decision. Decision curves and cost curves are model evaluation approaches that ass…

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