An Improved Method for Developing Injury Risk Curves Using the Brier Metric Score

Area of Science:

• Biomechanics
• Injury Biomechanics
• Human Subject Research

Background:

• Numerous injury metrics are derived from post mortem human subject (PMHS) experiments.
• Lack of clear guidance exists for selecting optimal parameters for human injury probability curves (HIPCs).

Purpose of the Study:

• To identify the most appropriate metric from experimental data for predicting injury outcomes using the Brier Metric Score (BMS).
• To establish a standardized procedure for developing human injury probability curves (HIPCs).

Main Methods:

• Utilized the Brier Metric Score (BMS) to evaluate and select predictive metrics.
• Performed survival analysis with the selected metric, identifying the best distribution via Akaike information criterion (AIC).
• Calculated confidence intervals (CIs) and normalized confidence interval width (NCIS) for the injury probability curve.

Main Results:

• The Brier Metric Score effectively identifies metrics that best predict injury outcomes in biomechanical experiments.
• The developed methodology provides a robust framework for generating reliable human injury probability curves.
• Validated using existing biomechanics data, demonstrating applicability to various ATDs and PMHS research.

Conclusions:

• The Brier Metric Score offers a quantitative approach to metric selection for human injury probability curve development.
• This methodology ensures more accurate and reliable injury risk assessments from biomechanical data.
• The procedure is adaptable for creating injury assessment reference curves for different anthropomorphic test devices (ATDs) and PMHS.