When psychological signals meet neurological disability: An explainable XGBoost study of dissociative symptoms in people with multiple sclerosis

: Dissociative symptoms provide modest but non-trivial predictive information for disability severity in PWMS beyond conventional indicators. Incorporating dissociation into predictive frameworks may support early risk stratification without implying causal relationships and may inform integrative clinical formulations. Explainable machine learning (e.g. XGBoost) can help characterize biopsychosocial patterns relevant to disability in chronic neurological disease. Findings warrant replication in longitudinal, multi-center cohorts.