{"id":"W4391529461","doi":"10.1101/2024.02.03.24302301","title":"Identifying Prediabetes in Canadian Populations Using Machine Learning","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Ted Rogers Centre for Heart Research; York University; Public Health Ontario; University of Toronto","funders":"","keywords":"Prediabetes; Type 2 diabetes; Machine learning; Medicine; Health care; Artificial intelligence; Identification (biology); Computer science; Diabetes mellitus; Biology; Endocrinology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002868041,0.0005951202,0.0003559605,0.002072089,0.001651368,0.001195065,0.001251423,0.0004477845,0.001976088],"category_scores_gemma":[0.01096334,0.0002133584,0.0009897971,0.003384332,0.0004617608,0.0004296385,0.0009020391,0.0009903385,0.0003024343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01965697,"about_ca_system_score_gemma":0.03604941,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.987344,"about_ca_topic_score_gemma":0.9903222,"domain_scores_codex":[0.9990453,0.0001809549,0.00005349563,0.0001756365,0.0003639587,0.0001806246],"domain_scores_gemma":[0.9975134,0.0007315945,0.000273833,0.0001341231,0.001174833,0.0001721084],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004073834,0.0001628938,0.7948947,0.0005554208,0.0004443581,0.0003624338,0.001221564,0.02001397,0.0005960094,0.006292456,0.02219566,0.1528532],"study_design_scores_gemma":[0.0001204178,0.0001455705,0.7897163,0.0007719578,0.0006865489,0.0003204839,0.00322715,0.1652388,0.001371998,0.0069321,0.03124989,0.0002187341],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9190999,0.008243709,0.01769065,0.008211746,0.000162495,0.0003490179,0.03078989,0.0002904072,0.0151622],"genre_scores_gemma":[0.9728373,0.003094071,0.01059611,0.0006527026,0.00004021918,0.00008500033,0.01085065,0.00002901029,0.001814803],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01965697,"threshold_uncertainty_score":0.142622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3588045041989029,"score_gpt":0.5207921480066552,"score_spread":0.1619876438077523,"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."}}