CKD risk prediction models achieve AUROC 1.00 internally but drop to 0.48-0.58 externally with high calibration error and low deployment scores, indicating need for external validation.
Machine learning to predict end stage kidney disease in chronic kidney disease
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Calibration, Uncertainty Communication, and Deployment Readiness in CKD Risk Prediction: A Framework Evaluation Study
CKD risk prediction models achieve AUROC 1.00 internally but drop to 0.48-0.58 externally with high calibration error and low deployment scores, indicating need for external validation.