The Performance Ceiling: Why Clinical Data Is Insufficient for Precision Prognosis in Concussion

Abstract

The transition to active rehabilitation in concussion care requires precise tools to identify patients atrisk of persistent post-concussive symptoms (PPCS). While machine learning (ML) offers thepotential to personalize prognosis, current models relying on clinical history and subjective symptomreporting (e.g., SCAT5) have failed to demonstrate significant performance gains over the lastdecade. This perspective article argues that clinical prognostic models have reached a performanceceiling of approximately 0.85 Area Under the Curve (AUC). By reviewing key studies from 2016 to2025, we demonstrate that increasing algorithmic complexity—from logistic regression to deeplearning—yields diminishing returns when applied to subjective inputs. In contrast, modelsincorporating physiological data, such as neuroimaging or fluid biomarkers, consistently break thisceiling, achieving AUCs exceeding 0.95. We conclude that better mathematics cannot correct formissing biological signal, and that the advancement of precision medicine in neurotrauma requires afundamental shift toward multimodal, biological data integration

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