{"id":"W1505456340","doi":"10.1111/j.1464-5491.2011.03568.x","title":"Using the Johns Hopkins’ Aggregated Diagnosis Groups (ADGs) to predict 1‐year mortality in population‐based cohorts of patients with diabetes in Ontario, Canada","year":2011,"lang":"en","type":"article","venue":"Diabetic Medicine","topic":"Chronic Disease Management Strategies","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre; Institute for Clinical Evaluative Sciences; University of Toronto","funders":"Canadian Institutes of Health Research; Health Canada; Ontario Ministry of Health and Long-Term Care; Institute for Clinical Evaluative Sciences","keywords":"Medicine; Logistic regression; Diabetes mellitus; Population; Cohort; Comorbidity; Demography; Statistic; Retrospective cohort study; Cohort study; Ambulatory; National Death Index; Gerontology; Hazard ratio; Internal medicine; Statistics; Confidence interval; Environmental health","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001330764,0.0004631028,0.0003232475,0.001262844,0.001181518,0.0009274826,0.000944255,0.0003108176,0.001071142],"category_scores_gemma":[0.00464923,0.000254345,0.0005792619,0.001769038,0.000427314,0.0002867294,0.0007710346,0.0005426986,0.0001748787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01211914,"about_ca_system_score_gemma":0.01603145,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9808061,"about_ca_topic_score_gemma":0.9846393,"domain_scores_codex":[0.9991823,0.0001259896,0.0000653243,0.0001431772,0.0003124692,0.0001708086],"domain_scores_gemma":[0.9977518,0.0002050215,0.0004525902,0.0001206758,0.001064025,0.0004059153],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005106286,0.000007474486,0.9962125,0.00001077378,0.00004013152,0.00001792742,0.00007738653,0.0002975352,0.0000370776,0.0000357531,0.0007784367,0.002433963],"study_design_scores_gemma":[0.00001819471,0.00001601865,0.9953632,0.00002305463,0.00003904412,0.000033016,0.0002257647,0.003492744,0.00005173611,0.00005833218,0.000669788,0.000008976114],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9904506,0.0006926711,0.0008157621,0.000519963,0.00003227251,0.00006875452,0.005593249,0.00003645126,0.001790405],"genre_scores_gemma":[0.9956468,0.0002414602,0.0008458453,0.00004312895,0.00001238487,0.0000213626,0.002654583,0.000004489355,0.0005299411],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01919389,"threshold_uncertainty_score":0.08793098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03644928568836995,"score_gpt":0.2570667458391828,"score_spread":0.2206174601508128,"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."}}