{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T05:55:31Z","timestamp":1789710931570,"version":"4.0.1"},"reference-count":46,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T00:00:00Z","timestamp":1773446400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T00:00:00Z","timestamp":1773446400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Annals of Neurology"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Pre\u2010mortem diagnosis of parkinsonism is often challenging due to atypical presentations, overlapping syndromes, and co\u2010pathologies. This study aimed to develop a machine learning\u2010based algorithm predicting neuropathology in parkinsonism using chronological clinical presentations, which has previously been underexplored.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Clinical information was automatically abstracted from medical records of the Mayo Clinic Brain Bank using fine\u2010tuned Generative Pre\u2010trained Transformer 4 models. Patients who developed parkinsonism within 3\u2009years of disease onset were included. Six machine learning models were trained with age, sex, family history, and 197 clinical presentations paired with onset information to predict neuropathologic diagnoses, including co\u2010pathologies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 7,825 donors, 949 met inclusion criteria, representing 9 neuropathologic categories: Lewy body disease (LBD; n\u2009=\u2009128), LBD with Alzheimer's disease (AD; n\u2009=\u2009136), progressive supranuclear palsy (PSP; n\u2009=\u2009303), PSP with AD (n\u2009=\u200956), PSP with LBD (n\u2009=\u200927), multiple system atrophy (MSA; n\u2009=\u2009120), corticobasal degeneration (CBD; n\u2009=\u200999), AD (n\u2009=\u200943), and frontotemporal lobar degeneration (FTLD; n\u2009=\u200937). The CatBoost algorithm achieved an area under the receiver operating characteristic curve of 0.83 across the 9 diagnostic categories at 3\u2009years after onset. Important predictors included age at onset, restricted eye movement, and tremor. The model remained robust to incomplete data, requiring only 23 of 200 parameters for reliable predictions with an area under the curve of 0.80. The algorithm was implemented into a user\u2010friendly program providing diagnostic probabilities with visualizations of parameter contributions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Interpretation<\/jats:title>\n                    <jats:p>This neuropathology\u2010confirmed diagnostic algorithm provides a cost\u2010effective and interpretable screening tool for parkinsonism, bridging biomarker testing and molecular\u2010targeted therapies. 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