{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003728095,0.0002224892,0.0003155021,0.0001252499,0.0008706046,0.0001530306,0.0001278456,0.0001054001,0.0002520951],"category_scores_gemma":[0.0001215644,0.0001898089,0.0001019546,0.00004668167,0.0002590278,0.0001648189,0.0002128351,0.0005520545,0.00001332041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001708992,"about_ca_system_score_gemma":0.00004259433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005487117,"about_ca_topic_score_gemma":0.0000177872,"domain_scores_codex":[0.9980767,0.0002063741,0.0002911986,0.000656736,0.0003341797,0.0004348068],"domain_scores_gemma":[0.9989789,0.0001110623,0.0001843623,0.0002756615,0.0002349587,0.0002150655],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003057273,0.002959721,0.9829405,0.00001036718,0.00004480115,0.0002960273,0.00005868712,3.105846e-7,0.002084875,0.00001072605,0.00006049988,0.008476198],"study_design_scores_gemma":[0.007989881,0.02093195,0.9690992,0.00001007043,0.0003521345,0.0002784387,0.0002422676,0.000670291,0.00005739813,0.00001051543,0.0002116918,0.0001461456],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951996,0.00003507845,0.0003489172,0.001807699,0.0002326531,0.001948527,0.00002195156,0.00007104439,0.0003345629],"genre_scores_gemma":[0.9977466,0.00002527303,0.000204934,0.0004671128,0.00008447059,0.0005242077,0.0001193171,0.0000307184,0.0007973672],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01797223,"threshold_uncertainty_score":0.774018,"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."}}