F1000Prime recommendation of Calibration of risk prediction models: impact on decision-analytic performance.

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Calibration: the Achilles heel of predictive analytics (2019, background): “Systematic reviews have found that calibration is assessed far less often than discrimination [2, 3, 5–7], which is problematic since poor calibration can make predictions misleading [8].”
Reporting and Interpreting Decision Curve Analysis: A Guide for Investigators (2018, unknown): “Importantly, when models are calibrated, they cannot be harmful [4,5].”
Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer. (2016, background): “However, the risk-prediction performance, discrimination and calibration, could not capture the clinical consequences of a particular level of discrimination or degree of miscalibration.(24,38,39) Therefore, to justify the clinical usefulness, we assessed whether the radiomics nomogram-assisted deci”