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Yet, their detection and characterization remain suboptimal because standard approaches rely on subjective impressions derived from lengthy, univariate tests.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>We examined whether digital analysis of verbal fluency predicts cognitive status in PD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We asked 464 Spanish speakers with PD to complete taxonomic (animal), thematic (supermarket), and phonemic (\/p\/) fluency tasks. We quantified six response properties: semantic variability, granularity, concreteness, length, frequency, and phonological neighborhood. In Study 1, these properties were fed to a ridge regressor to predict Mattis Dementia Rating Scale (MDRS) scores and subscores. In Study 2, we used the same properties to compare (via a generalized linear model) and classify (via random forest) between 123 patients with and 124 without MCI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In Study 1, predicted MDRS scores and subscores strongly correlated with actual ones, adjusting for clinical and cognitive variables (\n                      <jats:italic>R<\/jats:italic>\n                      \u2009=\u20090.51,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.001). In Study 2, MCI patients' words were less semantically variable, less concrete, and shorter, adjusting for clinical and cognitive variables (\n                      <jats:italic>P<\/jats:italic>\n                      \u2010values &lt;\u20090.05). Machine learning discrimination between patients with and without MCI was robust in the validation set (area under the curve [AUC]\u2009=\u20090.76), with good generalization to unseen pre\u2010surgical (AUC\u2009=\u20090.68) and post\u2010surgical (AUC\u2009=\u20090.72) samples, surpassing MDRS scores (AUC\u2009=\u20090.54). Results were consistently driven by semantic variability, granularity, and concreteness.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Digital word property analysis predicts cognitive symptom severity and distinguishes between cognitive phenotypes of PD, enabling scalable neuropsychological screenings. \u00a9 2025 International Parkinson and Movement Disorder Society.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mds.70005","type":"journal-article","created":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T05:58:46Z","timestamp":1755064726000},"page":"2354-2366","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Cognitive Phenotyping of Parkinson's Disease Patients Via Digital Analysis of Spoken Word 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