Multiple brain injury criteria (BIC) are developed to quickly quantify brain injury risks after head impacts. These BIC originated from different head impact types (e.g. sports and car crashes) are widely used in risk evaluation. However, the accuracy of using the BIC on brain injury risk estimation across head impact types has not been evaluated. Physiologically, brain strain is often considered the key parameter of brain injury. To evaluate the BIC's risk estimation accuracy across five datasets comprising different head impact types, linear regression was used to model 95% maximum principal strain, 95% maximum principal strain at the corpus callosum and cumulative strain damage (15%) on 18 BIC. The results show significantly different relationships between BIC and brain strain across datasets, indicating the same BIC value may suggest different brain strain across head impact types. The accuracy of brain strain regression is generally decreasing if the BIC regression models are fitted on a dataset with a different type of head impact rather than on the dataset with the same type. Given this finding, this study raises concerns for applying BIC to estimate the brain injury risks for head impacts different from the head impacts on which the BIC was developed.

制定了多种脑损伤标准 (BIC)，以快速量化头部撞击后的脑损伤风险。这些源自不同头部碰撞类型（例如运动和车祸）的BIC被广泛用于风险评估。然而，使用 BIC 评估各种头部撞击类型的脑损伤风险的准确性尚未得到评估。从生理学角度来看，脑应变通常被认为是脑损伤的关键参数。为了评估 BIC 在包含不同头部撞击类型的五个数据集上的风险估计准确性，使用线性回归对 18 个 BIC 上的 95% 最大主应变、胼胝体处的 95% 最大主应变和累积应变损伤 (15%) 进行建模。结果显示，不同数据集中的 BIC 和脑应变之间的关系存在显着差异，这表明相同的 BIC 值可能表明不同头部撞击类型的脑应变不同。如果将 BIC 回归模型拟合到具有不同类型头部影响的数据集而不是拟合到具有相同类型的数据集，则脑应变回归的准确性通常会降低。鉴于这一发现，本研究提出了应用 BIC 来估计头部撞击造成的脑损伤风险的担忧，该风险不同于 BIC 开发时所针对的头部撞击。