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This is a Quantitative Study study.

Improving Calibration Assessment near Clinical Thresholds: The Bayesian Calibration Error

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Introducing Bayesian calibration error improves detection of miscalibration in predictive models, highlighting risks near clinical thresholds.

Bayesian calibration error identifies miscalibration concentrated around clinical decision thresholds, enhancing model assessment.
Study of 3,672 pregnant women revealed adverse outcomes incidence of 13%, emphasizing the importance of accurate risk prediction.
The approach uses quantile-based adaptive binning and Bayesian estimation to improve local calibration error measurement.
This method encourages clinicians to report both absolute calibration error and Bayesian calibration error for informed decision-making.

Zamagni et al. (2025) studied this question.

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Introducing Bayesian calibration error improves detection of miscalibration in predictive models, highlighting risks near clinical thresholds.