Many injury metrics are routinely proposed from measured or derived quantities from biomechanical experiments using post mortem human subjects (PMHS). The existing literature did not provide guidance on deciding between parameters collected in an experiment that would be best to use for the development of human injury probability curves (HIPC). The objective of this study was to use the Brier Metric Score (BMS) to identify the most appropriate metric from an experiment that predicts injury outcomes. The Brier Metric Score assesses how well a metric predicts the outcome for a censored data point (a lower BMS is better). Survival analysis was then conducted with the selected metric and the best distribution was selected using Akaike information criterion (AIC). Confidence intervals (CIs) and the normalized confidence interval width (NCIS) were calculated for the injury probability curve. The testing and validation of the methods described were performed using biomechanics data in the open literature. The methods for the HIPC development procedure detailed herein have been rigorously tested and used in the generation of WIAMan HIPCs and Injury Assessment Reference Curves (IARCs) for the WIAMan ATD, but can also be used in other ATD or PMHS injury risk curve development.

许多伤害指标通常是从使用死后人类受试者 (PMHS) 的生物力学实验中测量或导出的数量提出的。现有文献没有提供关于在实验中收集的参数之间做出决定的指导，这些参数最适合用于开发人体伤害概率曲线 (HIPC)。本研究的目的是使用 Brier 指标评分 (BMS) 从预测损伤结果的实验​​中确定最合适的指标。Brier Metric Sc​​ore 评估指标对审查数据点结果的预测效果（BMS 越低越好）。然后使用所选指标进行生存分析，并使用 Akaike 信息准则 (AIC) 选择最佳分布。计算损伤概率曲线的置信区间 (CI) 和归一化置信区间宽度 (NCIS)。使用公开文献中的生物力学数据对所描述的方法进行测试和验证。本文详述的 HIPC 开发过程的方法已经过严格测试，并用于生成 WIAMan HIPC 和伤害评估参考曲线 (IARC)，用于 WIAMan ATD，但也可用于其他 ATD 或 PMHS 伤害风险曲线开发。