{"id":"W2275188228","doi":"10.1111/1475-6773.12461","title":"Development and Validation of HealthImpact: An Incident Diabetes Prediction Model Based on Administrative Data","year":2016,"lang":"en","type":"article","venue":"Health Services Research","topic":"Diabetes, Cardiovascular Risks, and Lipoproteins","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Tellabs (Canada)","funders":"","keywords":"Medicine; Statistic; Diabetes mellitus; Cohort; Cohort study; Pharmacy; Statistics; Emergency medicine; Internal medicine; Family medicine; Mathematics","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":[0.009634339,0.001061734,0.0008334483,0.002001722,0.0003556784,0.001413157,0.001520341,0.0004456294,0.001379664],"category_scores_gemma":[0.01476711,0.0004576447,0.001174359,0.001436745,0.0003228705,0.0006511866,0.001123303,0.001068675,0.000347583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001353765,"about_ca_system_score_gemma":0.003335932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01586579,"about_ca_topic_score_gemma":0.009822507,"domain_scores_codex":[0.9982877,0.0006776725,0.0001262804,0.0003414983,0.0004372633,0.0001295537],"domain_scores_gemma":[0.9947788,0.003014867,0.0006797657,0.0004129687,0.0008620893,0.0002515666],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001149467,0.001008098,0.7891514,0.0001282179,0.0006555018,0.0001791584,0.0001257002,0.1493596,0.0005324003,0.001104754,0.005368368,0.05123742],"study_design_scores_gemma":[0.0002997835,0.0006726424,0.1170321,0.00007736483,0.0002325741,0.0001467713,0.00007428072,0.8774858,0.0008370252,0.001742238,0.001357622,0.00004174367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8749579,0.0003424105,0.1104387,0.0009112183,0.0001125557,0.0007966752,0.008934903,0.001157463,0.002348193],"genre_scores_gemma":[0.931744,0.0002195672,0.05706415,0.0001289709,0.00006271435,0.0003642312,0.009748212,0.00004477938,0.0006232857],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01586579,"threshold_uncertainty_score":0.0509519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.166342317273437,"score_gpt":0.4421505678056263,"score_spread":0.2758082505321894,"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."}}