{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005036123,0.0001394945,0.0003916762,0.0003156638,0.0002269511,0.00002898974,0.0002303129,0.00009781247,0.00002146048],"category_scores_gemma":[0.00009359723,0.00009684568,0.00003160475,0.0002626754,0.00009372847,0.000324235,0.0001299101,0.0002230688,0.0000106181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002805789,"about_ca_system_score_gemma":0.001790802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002146885,"about_ca_topic_score_gemma":0.001122161,"domain_scores_codex":[0.9966952,0.000428788,0.0004729489,0.0005236868,0.001342193,0.0005372305],"domain_scores_gemma":[0.9977025,0.0001816507,0.0001764565,0.001018297,0.0003600905,0.0005610177],"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":[0.0008774436,0.0009403469,0.03598164,0.006869928,0.000161519,0.000005439169,0.006809202,0.0001361852,0.001045136,0.00006423607,0.0002160946,0.9468929],"study_design_scores_gemma":[0.005852382,0.00649248,0.7385956,0.005574736,0.00005523284,0.000004837729,0.002539922,0.1841619,0.04984532,0.0002209252,0.006322982,0.0003336408],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952244,0.0004311989,0.0005229673,0.00216375,0.00003781627,0.001211759,0.0002089573,0.00004190094,0.000157231],"genre_scores_gemma":[0.9946491,0.000292976,0.003760351,0.0005702989,0.0001188558,0.00008414424,0.0004604183,0.00002478394,0.00003911991],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9465592,"threshold_uncertainty_score":0.3949252,"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."}}