Evidence synthesis on sex differences in mortality after traumatic brain injury: Systematic review integrating meta-analysis and explainable machine learning

Sex differences in mortality after traumatic brain injury (TBI) have been inconsistently reported, even after accounting for age, injury severity, and other injury related factors. Understanding the sources of this heterogeneity is important for prognostic research and the development of data-driven clinical decision-support tools. We conducted a systematic review and meta-analysis of sex-specific mortality after adult TBI and used explainable machine learning (ML) to investigate potential modifiers of between-study effect estimates. We searched eight databases from inception to August 25, 2025, for studies reporting mortality estimates adjusted for at least age and injury severity. Risk of bias was assessed using the Quality in Prognosis Studies tool. Male-to-female mortality estimates were pooled using random-effects meta-analysis. Three explainable ML algorithms were used to identify important modifiers of study-level effect estimates. Among 9,418 records identified, 21 studies comprising 899,813 participants and 98,286 deaths met the inclusion criteria. Pooled analyses showed lower mortality among females than males (OR 0.88, 95% CI 0.80-0.98; HR 0.76, 95% CI 0.59-0.98). Lower mortality among females was also observed for in-hospital mortality (OR 0.86, 95% CI 0.76-0.97) and overall or unspecified mortality (OR 0.83, 95% CI 0.78-0.88). Substantial residual heterogeneity remained across analyses. Explainable ML identified mean age, study sex composition, and study quality as important modifiers of reported effect estimates. These findings were consistent across sensitivity analyses. Female sex was associated with lower mortality after adult TBI, although substantial between-study heterogeneity remained. Explainable ML identified demographic and methodological factors that may contribute to variation in reported associations. These findings highlight the importance of population characteristics and study context when developing, validating, and interpreting explainable digital prognostic tools for TBI. PROSPERO: CRD420251252136.