{"id":"W4385421310","doi":"10.3390/brainsci13081139","title":"Detection of Alzheimer’s Disease Using Logistic Regression and Clock Drawing Errors","year":2023,"lang":"en","type":"article","venue":"Brain Sciences","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":15,"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; Eisai; Bulgarian National Science Fund; Northern California Institute for Research and Education; 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":"Logistic regression; Receiver operating characteristic; Dementia; Verbal fluency test; Disease; Fluency; Area under the curve; Medicine; Statistics; Psychology; Cognition; Neuropsychology; Internal medicine; Psychiatry; Mathematics","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":[0.02403195,0.002599144,0.001712635,0.004196503,0.0004655111,0.002285902,0.002220233,0.001306251,0.001377913],"category_scores_gemma":[0.06245215,0.0007266638,0.004032093,0.002887223,0.0009131373,0.001714377,0.002031458,0.002319491,0.0006946049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001069185,"about_ca_system_score_gemma":0.001371634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006657724,"about_ca_topic_score_gemma":0.002772371,"domain_scores_codex":[0.9844701,0.01171631,0.0007991184,0.001688604,0.000739707,0.0005861867],"domain_scores_gemma":[0.9251144,0.06304363,0.006847008,0.00250076,0.001860848,0.0006333431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004739819,0.0007167305,0.6092629,0.0003068175,0.002233353,0.001156753,0.0006386901,0.2838669,0.001219485,0.002374334,0.001270573,0.09221363],"study_design_scores_gemma":[0.00006710696,0.0005430193,0.03164305,0.00005765649,0.0003307902,0.0003534361,0.0001345523,0.9632195,0.0007173078,0.002375771,0.0004852855,0.00007259681],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.793241,0.001278524,0.2011871,0.0007922177,0.0001308877,0.0003374411,0.0007804385,0.0009097939,0.001342587],"genre_scores_gemma":[0.9623391,0.0003294316,0.0358009,0.00006309969,0.00005495167,0.0001453353,0.0005488078,0.00005546739,0.0006628832],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02403195,"threshold_uncertainty_score":0.1270946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1445485199644938,"score_gpt":0.42566407286034,"score_spread":0.2811155528958462,"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."}}