{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007642166,0.0004853473,0.0006597945,0.0001066437,0.001716236,0.00009662122,0.00047136,0.0002184964,0.002514392],"category_scores_gemma":[0.0005779545,0.0004765524,0.0003015928,0.0002956944,0.00009900798,0.0004022978,0.001437638,0.001526231,0.0008144139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002017535,"about_ca_system_score_gemma":0.0002580445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007857798,"about_ca_topic_score_gemma":0.001247856,"domain_scores_codex":[0.9942399,0.001548971,0.001216031,0.00121461,0.0006816409,0.001098862],"domain_scores_gemma":[0.9968374,0.001054521,0.0004720402,0.0009107657,0.0002861897,0.0004390378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001812277,0.0002565077,0.9730248,0.00004011832,0.01430492,0.00006947928,0.002744965,0.00003006728,0.0001039825,0.0000712445,0.002322547,0.007013174],"study_design_scores_gemma":[0.001566374,0.00002787342,0.9520159,0.0005956841,0.02627155,0.000001994605,0.002003576,0.002584329,0.0002725193,0.0004231027,0.01350769,0.0007293588],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.4380109,0.5291723,0.01458927,0.003310204,0.005595995,0.00226182,0.0002551653,0.001406981,0.005397389],"genre_scores_gemma":[0.9900967,0.00079373,0.005911025,0.001112193,0.0008628201,0.0003429808,0.0005827105,0.0001350274,0.0001627605],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5520859,"threshold_uncertainty_score":0.9999636,"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."}}