{"id":"W3111412015","doi":"10.23889/ijpds.v5i5.1450","title":"Using Linked Administrative Health Databases for An Obesity Case Definition","year":2020,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Health Promotion and Cardiovascular Prevention","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Medicine; Medical prescription; Obesity; Body mass index; Diagnosis code; Population; Health care; Database; Family medicine; Pediatrics; Environmental health; Internal medicine; Computer science","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":[0.02467911,0.0006958387,0.0007681019,0.009807135,0.001066494,0.003452595,0.002059024,0.0007383497,0.00400031],"category_scores_gemma":[0.08115212,0.0005351494,0.00170153,0.009959953,0.000456561,0.001852388,0.00297995,0.001063321,0.0009481297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002625928,"about_ca_system_score_gemma":0.004352667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01888084,"about_ca_topic_score_gemma":0.0160717,"domain_scores_codex":[0.9638935,0.01474351,0.008326112,0.004148501,0.007842707,0.001045678],"domain_scores_gemma":[0.928708,0.02021212,0.03000381,0.007471471,0.01258228,0.001022304],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003834786,0.0003769106,0.9115621,0.001185613,0.0006806642,0.0002937544,0.001186228,0.002389004,0.0005713825,0.00414894,0.01390313,0.06331889],"study_design_scores_gemma":[0.0003321713,0.0004260378,0.9048302,0.002273215,0.0007289143,0.001165418,0.002242527,0.02518384,0.003922784,0.006529739,0.05219331,0.0001719707],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7453726,0.003264705,0.09337901,0.003411842,0.0006044116,0.01521205,0.1163904,0.000940288,0.0214248],"genre_scores_gemma":[0.7742068,0.001238344,0.1245256,0.001032422,0.0003372826,0.01284389,0.08380968,0.0001153605,0.001890637],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02467911,"threshold_uncertainty_score":0.1305172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6411845115247103,"score_gpt":0.5615565348109857,"score_spread":0.07962797671372468,"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."}}