{"id":"W4405489189","doi":"10.1109/embc53108.2024.10782174","title":"Identifying Prediabetes in Canadian Populations Using Machine Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Diabetes, Cardiovascular Risks, and Lipoproteins","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"","keywords":"Prediabetes; Computer science; Artificial intelligence; Machine learning; Biology; Diabetes mellitus","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":[0.002752153,0.0005743604,0.0003504431,0.002135557,0.001857098,0.001187045,0.001169028,0.0004354294,0.001883614],"category_scores_gemma":[0.01067986,0.0002133891,0.0009941786,0.003379332,0.0004459005,0.0004586676,0.0008707772,0.0009689111,0.0002917041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02101229,"about_ca_system_score_gemma":0.03709371,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9885607,"about_ca_topic_score_gemma":0.9915724,"domain_scores_codex":[0.9990808,0.0001655196,0.00005249638,0.0001620521,0.0003545425,0.0001845895],"domain_scores_gemma":[0.9975114,0.0006740448,0.0002640072,0.0001320685,0.001240325,0.0001780683],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003606671,0.0001499993,0.818325,0.0004407803,0.0003647286,0.0003327813,0.001181335,0.01604402,0.0005392275,0.005581944,0.01670414,0.1399753],"study_design_scores_gemma":[0.00009942059,0.0001322312,0.831705,0.0006333975,0.0006267183,0.0003053729,0.003050921,0.1305774,0.001190361,0.005908958,0.02557813,0.0001920832],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9318684,0.00683015,0.01473157,0.007191909,0.0001382217,0.0003124377,0.02447036,0.0002413512,0.01421557],"genre_scores_gemma":[0.9786443,0.002636101,0.008712114,0.000541642,0.00003394777,0.00007435524,0.007858134,0.00002277364,0.001476585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02101229,"threshold_uncertainty_score":0.1524556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0551666008604751,"score_gpt":0.3148822359709861,"score_spread":0.259715635110511,"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."}}