RISED operationalizes five evaluation dimensions for AI decision-support systems via BCa bootstrap intervals and Holm-Bonferroni verdicts, revealing failures missed by AUROC on seven cohorts including Diabetes 130 and NHANES.
Sample size for binary logistic prediction models: Beyond events per variable criteria
3 Pith papers cite this work. Polarity classification is still indexing.
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Simulation study shows regression shrinkage improves average calibration of binary clinical models but raises between-sample variability and often applies the wrong amount of shrinkage in individual datasets.
Bootstrap-based comparison on real clinical data shows linear modeling of continuous predictors yields stable predictions at smaller sample sizes than more complex methods.
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On the variability of regression shrinkage methods for clinical prediction models: simulation study on predictive performance
Simulation study shows regression shrinkage improves average calibration of binary clinical models but raises between-sample variability and often applies the wrong amount of shrinkage in individual datasets.
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Influence of continuous predictor modelling methods on prediction stability in clinical prediction model development: an empirical comparison using real clinical data
Bootstrap-based comparison on real clinical data shows linear modeling of continuous predictors yields stable predictions at smaller sample sizes than more complex methods.