LAUSR

Background: In automotive events, head injuries (skull fractures and/or brain injuries) are associated with head contact loading. While the widely‐used head injury criterion is based on frontal bone fracture and linear accelerations, injury risk curves were not developed from original datasets. Objectives: Develop skull fracture‐based risk curves for using previously published data and apply resampling techniques to assess their qualities. Methods: Force, deflection, energy, and stiffness data from thirteen human cadaver head impact tests were used to develop risk curves using parametric survival analysis. Injuries occurred to all specimens. Data points were treated as uncensored. Variables were ranked, and the variable best explaining the underlying fracture response was determined using the Brier Score Metric (BSM). The qualities of the risk curves were determined using normalized confidence interval sizes. Statistical resampling methods were used to assess the quality of the risk curves and the impact of the sample size by conducting 2000 simulations. Sample sizes ranged from 13 to 26. Findings: The Weibull distribution was optimal for all the response variables, except deflection (log‐logistic). The quality of the risk curves was the highest for deflection. This variable best explained the underlying head injury response, based on BSM. Improvements in the quality of the risk curves were achieved with additional samples of force and deflection (<13), while energy and stiffness variables required more size. Individual risk curves are given. Interpretation: These probability curves from head contact loading add to the understanding skull fractures and can be used to improve safety in injury producing environments. HIGHLIGHTSDevelop risk curves from parametric survival analysis for human frontal bone skull fracture.Determine the metric that best represents the underlying human response to head injury using the Brier Score Metric.Assess the qualities of the probability curves for different response variables.Use statistical resampling methods to achieve risk curves of desired quality.