{"id":"W2779914574","doi":"10.1097/md.0000000000009440","title":"Chart validation of inpatient ICD-9-CM administrative diagnosis codes for ischemic stroke among IGIV users in the Sentinel Distributed Database","year":2017,"lang":"en","type":"article","venue":"Medicine","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Food and Drug Administration; Hamilton Health Sciences Foundation; Kaiser Permanente; National Institute of Neurological Disorders and Stroke; U.S. Department of Health and Human Services","keywords":"Medicine; Medical diagnosis; Confidence interval; Diagnosis code; Medical record; Medical prescription; Emergency medicine; Stroke (engine); Population; Gold standard (test); Database; Chart; Pediatrics; Internal medicine; Pathology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00788576,0.0003220961,0.0004947289,0.001854723,0.000341282,0.0008563579,0.0006930636,0.0002784586,0.0007356338],"category_scores_gemma":[0.03212068,0.0003012998,0.0004568809,0.00142976,0.0004203161,0.0003738904,0.0007507366,0.0004373453,0.0002675529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008052122,"about_ca_system_score_gemma":0.001122749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008470236,"about_ca_topic_score_gemma":0.006367023,"domain_scores_codex":[0.9912016,0.0038451,0.001066901,0.001060921,0.002491493,0.0003339066],"domain_scores_gemma":[0.9552754,0.01943906,0.01039123,0.005007865,0.009021527,0.000864949],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002911162,0.00009252515,0.9932184,0.00003125562,0.00006989969,0.00007589182,0.0002427045,0.0004133897,0.0005719126,0.00009193557,0.0008272647,0.004073728],"study_design_scores_gemma":[0.00005693844,0.000315222,0.9906642,0.00005020378,0.00005000201,0.0002409807,0.000211573,0.005505621,0.001630812,0.00006535337,0.001194532,0.00001438572],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905238,0.0000864569,0.002161303,0.00004499901,0.00001876548,0.0003031857,0.005494283,0.00005616139,0.001310987],"genre_scores_gemma":[0.9844257,0.00007092931,0.002692102,0.00005889788,0.00002049187,0.0002072948,0.01231884,0.00001518036,0.000190628],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008470236,"threshold_uncertainty_score":0.04170436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05253483998714465,"score_gpt":0.3389626717062689,"score_spread":0.2864278317191242,"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."}}