Discrimination and Calibration of Clinical Prediction Models: Users’ Guides to the Medical Literature

By A. Alba, T. Agoritsas, M. Walsh, S. Hanna, A. Iorio, P. Devereaux, T. McGinn, G. Guyatt. 2017

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Learning Optimized Risk Scores (2016, background): “…to produce calibrated risk estimates, which are used to choose an operating point and to inform decision-making in other ways (see e.g., Section 6 and Van Calster and Vickers, 2015; Alba et al., 2017, for a discussion on how miscalibrated risk estimates can lead to harmful decisions in medicine).”
Supervised machine learning: A brief primer (2020, background): “Poor calibration may lead to underestimation or overestimation of the outcome of interest (Alba et al., 2017).”
A tutorial on calibration measurements and calibration models for clinical prediction models (2020, background): “Calibration is an essential component of the evaluation of computational models for medical decision making, diagnosis, and prognosis.(1,2) In contrast to discrimination, which refers to the ability of a model to rank patients according to risk, calibration refers to the agreement between the estima”