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• The NeuroPose-AHM dataset contains 2,756 patient-group-level records spanning 57 neurological conditions, derived from 846 AHM-relevant papers.result 95%
• Study-level classification in the multi-LLM extraction framework achieved strong agreement with kappa = 0.822.result 95%
• Multi-label AHM type classification for cervical dystonia achieved F1 score of 0.856.result 95%
• The Head-Neck Severity Index (HNSI) was validated against real-world CD patient data with aligned severe-band proportions of 6.7%.result 90%
• Bridge analysis between movement-type probabilities and HNSI scores produced significant correlations with p < 0.001.result 95%
• The NeuroPose-AHM dataset was constructed through a multi-LLM extraction framework applied to 1,430 peer-reviewed publications.method 95%
• The absence of a multi-condition resource integrating kinematic measurements, clinical severity scores, and patient demographics constitutes a persistent barrier to AI-driven diagnostic tools for abnormal head movements.limitation 85%
• Cervical dystonia is the neurological condition most directly defined by pathological head movement.technical 90%

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