{"id":"W2053658037","doi":"10.1186/1471-2288-9-62","title":"Optimal strategy to identify incidence of diagnostic of diabetes using administrative data","year":2009,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Medicine; Diabetes mellitus; Incidence (geometry); McNemar's test; Medical record; Emergency medicine; Retrospective cohort study; Disease; Cohort; Diagnosis code; Pediatrics; Medical emergency; Family medicine; Internal medicine; Environmental health; Statistics; Population","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.07056766,0.0001265118,0.0007313181,0.0003954169,0.0003330233,0.000005034157,0.00125597,0.0005050178,0.001558209],"category_scores_gemma":[0.5451505,0.0001016528,0.00004149207,0.0007496717,0.000576127,0.0001824095,0.0006591815,0.001744253,0.00007120497],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009856081,"about_ca_system_score_gemma":0.007129434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005107707,"about_ca_topic_score_gemma":0.0003270759,"domain_scores_codex":[0.9754809,0.01903395,0.001746629,0.0004060395,0.002249414,0.001083053],"domain_scores_gemma":[0.7990652,0.197147,0.0004781621,0.0009817336,0.00107791,0.001249974],"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":[0.003161185,0.001532214,0.219023,0.02239654,0.0002295376,0.0001328033,0.03213781,0.001314364,0.02849584,0.08343391,0.05973037,0.5484124],"study_design_scores_gemma":[0.005661638,0.01569938,0.7304552,0.01611389,0.0001496,0.00002196556,0.03261926,0.1576934,0.006951153,0.02755776,0.006127678,0.0009490583],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8159818,0.0002776433,0.1796986,0.001874715,0.0002886751,0.0008513448,0.00005149088,0.00002161401,0.0009541657],"genre_scores_gemma":[0.868274,0.0001793031,0.1302627,0.0008148818,0.000312915,0.00003953264,0.00004630549,0.000008938679,0.00006149007],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5474634,"threshold_uncertainty_score":0.9993545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9452251642253298,"score_gpt":0.7572481249518934,"score_spread":0.1879770392734363,"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."}}