{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T06:27:45Z","timestamp":1786429665556,"version":"build-2736575974"},"reference-count":56,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T00:00:00Z","timestamp":1786406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:p>\n                    Abnormal head movements (AHMs) manifest across a broad spectrum of neurological disorders, however the absence of a multi-condition resource integrating kinematic measurements, clinical severity scores, and patient demographics constitutes a persistent barrier to the development of AI-driven diagnostic tools. To address this gap, this study introduces NeuroPose-AHM, a knowledge-based dataset of neurologically induced AHMs constructed through a multi-LLM extraction framework applied to 1,430 peer-reviewed publications. The dataset contains 2,763 patient-group-level records spanning 57 neurological conditions, derived from 846 AHM-relevant papers. Inter-LLM reliability analysis confirms robust extraction performance, with study-level classification achieving strong agreement (\n                    <jats:italic>\u03ba<\/jats:italic>\n                    \u2009=\u20090.822). To demonstrate the dataset's analytical utility, a four-task framework is applied to cervical dystonia (CD), the AHM-related condition most directly caused by patterned dystonic muscle contraction. First, Task 1 performs multi-label AHM type classification (F1\u2009=\u20090.750). Task 2 constructs the Head\u2013Neck Severity Index (HNSI), a unified metric that normalizes heterogeneous clinical rating scales. The clinical relevance of this index is then evaluated in Task 3, where HNSI is compared against real-world CD patient data, with aligned severe-band proportions (6.7%) providing a preliminary plausibility indication that the ordinal heuristic could be appropriately calibrated within the high severity range. Finally, Task 4 performs bridge analysis between movement-type probabilities and HNSI scores, producing significant correlations (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&amp;lt;\u20090.001). These results demonstrate the analytical utility of NeuroPose-AHM as a structured, knowledge-based resource for neurological AHM research. The NeuroPose-AHM dataset is publicly available on Zenodo (\n                    <jats:ext-link>https:\/\/doi.org\/10.5281\/zenodo.19386862<\/jats:ext-link>\n                    ).\n                  <\/jats:p>","DOI":"10.3389\/fdgth.2026.1884459","type":"journal-article","created":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T05:35:02Z","timestamp":1786426502000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Abnormal head movements in neurological conditions: a knowledge-based dataset with application to cervical dystonia"],"prefix":"10.3389","volume":"8","author":[{"given":"Saja","family":"Al-Dabet","sequence":"first","affiliation":[{"name":"College of Information Technology, United Arab Emirates University","place":["Al Ain, United Arab Emirates"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sherzod","family":"Turaev","sequence":"additional","affiliation":[{"name":"College of 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