Net benefit is widely used and reported to evaluate the clinical utility of prediction models, yet its interpretation often remains difficult in practice. In this didactical note, we develop two complementary interpretations that make net benefit easier to understand for clinical audiences. We show that comparisons with treat-none and treat-all can be expressed through threshold-specific observed risk in patients above and below the decision threshold, linking decision-curve performance to calibration in clinically relevant subgroups. We also show how net benefit relates to positive predictive value, offering a more intuitive explanation of when acting on model predictions is justified. We derive and illustrate these results and propose positive predictive value curves as a practical complement to decision curves.

翻译：净效益被广泛用于评估预测模型的临床效用，但其实际解读仍常面临困难。在本教学笔记中，我们提出了两种互补的解读方式，以使临床受众更易理解净效益。我们证明，与“不治疗”和“全部治疗”的比较可通过决策阈值以上及以下患者中阈值特异性的观察风险来表达，从而将决策曲线性能与临床相关亚组的校准联系起来。我们还展示了净效益如何与阳性预测值相关联，为模型预测结果值得被采纳的时机提供了更直观的解释。我们推导并阐释了这些结果，同时提出将阳性预测值曲线作为决策曲线的一种实用补充。