{"id":"W2461518869","doi":"10.1017/cem.2016.50","title":"LO013: Can you trust administrative data? Accuracy of ICD-10 codes for diagnosis of pulmonary embolism","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Emergency Medicine","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Medicine; Medical diagnosis; Diagnosis code; Pulmonary embolism; ICD-10; Coding (social sciences); Medical record; Emergency department; Current Procedural Terminology; Chart; Emergency medicine; Medical emergency; Pediatrics; Surgery; Pathology; Statistics; Population; Psychiatry","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02780622,0.0001792229,0.0004007929,0.002170518,0.0007956552,0.002693554,0.001205012,0.001666447,0.007537979],"category_scores_gemma":[0.2795099,0.0004026362,0.0005397785,0.004669508,0.0007690269,0.003552178,0.001832052,0.001984808,0.002628282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002359879,"about_ca_system_score_gemma":0.005251299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1132598,"about_ca_topic_score_gemma":0.1254728,"domain_scores_codex":[0.9746929,0.01109509,0.005181726,0.001422142,0.005980388,0.001627703],"domain_scores_gemma":[0.7796484,0.1002839,0.05160634,0.01691117,0.04587055,0.005679608],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002071791,0.00003750761,0.819806,0.0001377494,0.0001205408,0.0001539867,0.001382863,0.000137734,0.0001641412,0.001681665,0.1298627,0.04630785],"study_design_scores_gemma":[0.0001449827,0.00009253925,0.8658393,0.001510286,0.0003149142,0.000811979,0.007362992,0.005441912,0.0006210641,0.004162412,0.1135787,0.0001187577],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.3399982,0.004296069,0.00936884,0.5636376,0.003731482,0.0001525377,0.03255029,0.0003785359,0.04588651],"genre_scores_gemma":[0.9535608,0.001534372,0.00647323,0.02229626,0.001477665,0.0000873508,0.0100454,0.0002431485,0.004281678],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9721938,"threshold_uncertainty_score":0.225201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4802149719866473,"score_gpt":0.5206202053957765,"score_spread":0.04040523340912916,"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."}}