{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007166784,0.00005304702,0.0000897804,0.0002176062,0.0001988409,0.00002208964,0.00004583364,0.00001678047,0.00002344766],"category_scores_gemma":[0.0005449611,0.00003722557,0.00002766341,0.0006381523,0.0004301626,0.0000968591,0.00006492266,0.00004481652,0.000006292666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008162489,"about_ca_system_score_gemma":0.00009682227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003821645,"about_ca_topic_score_gemma":0.000002867473,"domain_scores_codex":[0.9990701,0.00005129095,0.0001094927,0.000189663,0.0003993642,0.0001800702],"domain_scores_gemma":[0.9996299,0.0001040672,0.00004166614,0.00006520113,0.00004747998,0.0001117209],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001539939,0.00008468327,0.5483852,0.0001583095,0.00003429007,0.0001007163,0.0004085164,0.00005392528,0.3734303,0.0001750739,0.0002787308,0.07673622],"study_design_scores_gemma":[0.0003956825,0.0002794698,0.9540154,0.0002554451,0.00006688947,0.0000108592,0.0009928205,0.02289358,0.0202216,0.0006381567,0.0001598388,0.00007024316],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997751,0.00039237,0.000287564,0.0009300764,0.00006936738,0.0001699254,0.000001575126,0.00002977409,0.0003683285],"genre_scores_gemma":[0.9995539,0.00002688909,0.0001361272,0.00007999905,0.0000295091,0.000004187074,0.000001579102,0.000003309211,0.000164535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4056302,"threshold_uncertainty_score":0.1584952,"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."}}