{"id":"W4205572690","doi":"10.1002/alz.057316","title":"Identifying preclinical Alzheimer disease from driving patterns: A machine learning approach","year":2021,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Older Adults Driving Studies","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"","keywords":"Medicine; Dementia; Disease; Biomarker; Random forest; Cohort; Feature (linguistics); Machine learning; Pathology; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001481042,0.0007881788,0.00062305,0.001956378,0.0002993814,0.000627947,0.0006784779,0.0007507442,0.0009106927],"category_scores_gemma":[0.003572856,0.0001985256,0.000858047,0.0008479357,0.0002084488,0.0003609223,0.0003220491,0.0005955086,0.0003964739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003456721,"about_ca_system_score_gemma":0.0004863516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005975003,"about_ca_topic_score_gemma":0.00354099,"domain_scores_codex":[0.9995129,0.0001939928,0.00004776844,0.0001276488,0.00004426133,0.00007342003],"domain_scores_gemma":[0.9979001,0.001520848,0.0001663269,0.00008669987,0.0002594848,0.00006655655],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007785601,0.001023174,0.2759712,0.0001581541,0.0005829794,0.0003678594,0.0001868646,0.2805332,0.005415143,0.0005132176,0.002029534,0.43244],"study_design_scores_gemma":[0.00001872176,0.0001634469,0.03384697,0.00002036504,0.00006259363,0.0001078786,0.000056141,0.9639111,0.0007618389,0.0007793526,0.0002531435,0.00001834859],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7670122,0.001057702,0.2284722,0.0004043913,0.00006941798,0.0001617712,0.0008636717,0.0007323631,0.001226275],"genre_scores_gemma":[0.9631857,0.0001296772,0.03531653,0.00004303263,0.00004094362,0.00007376307,0.0007030598,0.00001134953,0.0004959499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005975003,"threshold_uncertainty_score":0.01188046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1191755328747129,"score_gpt":0.4046397525983255,"score_spread":0.2854642197236125,"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."}}