{"id":"W2583500168","doi":"10.1155/2017/1850909","title":"Optimizing Neuropsychological Assessments for Cognitive, Behavioral, and Functional Impairment Classification: A Machine Learning Study","year":2017,"lang":"en","type":"article","venue":"Behavioural Neurology","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":119,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Consiglio Nazionale delle Ricerche; Eisai; Northern California Institute for Research and Education; DoD Alzheimer's Disease Neuroimaging Initiative; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; Eli Lilly and Company; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Neuropsychology; Dementia; Neuropsychological assessment; Cognition; Psychology; Clinical Dementia Rating; Neuropsychological test; Cognitive impairment; Clinical psychology; Disease; Psychiatry; Medicine; Pathology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008115895,0.000853996,0.0007816841,0.001042281,0.0002850515,0.0007222001,0.0005578774,0.0006168969,0.0004101934],"category_scores_gemma":[0.02284735,0.0001976192,0.0007137056,0.0008660537,0.0004308501,0.00100219,0.0004691907,0.0007468958,0.0001871284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00057377,"about_ca_system_score_gemma":0.0008513705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002857285,"about_ca_topic_score_gemma":0.002117305,"domain_scores_codex":[0.9965522,0.002394722,0.0002249971,0.000387332,0.000261882,0.0001789029],"domain_scores_gemma":[0.9805918,0.01641861,0.0009281722,0.0007048725,0.0009756585,0.0003809936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.006489917,0.00424932,0.7551485,0.0001562877,0.0006138748,0.0001480505,0.000269306,0.02728426,0.001990414,0.0002221063,0.0006327527,0.2027953],"study_design_scores_gemma":[0.000318568,0.00710049,0.5346714,0.00006261065,0.0005563857,0.0003748054,0.000317161,0.4519887,0.002971967,0.001071019,0.0004991354,0.00006772453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946154,0.0004269037,0.00442941,0.00008358886,0.00001178753,0.00006126314,0.0000810317,0.00002426807,0.0002664621],"genre_scores_gemma":[0.9952908,0.0001473457,0.004116976,0.00003051026,0.00002251768,0.00003485718,0.0002467416,0.000006948251,0.0001033516],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008115895,"threshold_uncertainty_score":0.04292148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1783563587033664,"score_gpt":0.4402015880913794,"score_spread":0.261845229388013,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}