BACKGROUND The assessment of calibration performance of risk prediction models based on regression or more flexible machine learning algorithms receives little attention. MAIN TEXT Herein, we argue that this needs to change immediately because poorly calibrated algorithms can be misleading and potentially harmful for clinical decision-making. We summarize how to avoid poor calibration at algorithm development and how to assess calibration at algorithm validation, emphasizing balance between model complexity and the available sample size. At external validation, calibration curves require sufficiently large samples. Algorithm updating should be considered for appropriate support of clinical practice. CONCLUSION Efforts are required to avoid poor calibration when developing prediction models, to evaluate calibration when validating models, and to update models when indicated. The ultimate aim is to optimize the utility of predictive analytics for shared decision-making and patient counseling.

中文翻译：

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校准：预测分析的致命弱点。

背景技术基于回归或更灵活的机器学习算法的风险预测模型的校准性能评估很少受到关注。正文在这里，我们认为这需要立即改变，因为校准不当的算法可能会误导人，并可能对临床决策产生危害。我们总结了如何避免算法开发中的标定不良以及如何在算法验证时评估标定，强调了模型复杂性和可用样本量之间的平衡。在外部验证时，校准曲线需要足够大的样本。应考虑更新算法，以为临床实践提供适当的支持。结论在开发预测模型时需要尽力避免校准不佳，在验证模型时要评估校准，并在指示时更新模型。最终目的是优化预测分析的效用，以实现共同的决策和患者咨询。