{"id":"W2392954647","doi":"10.1177/0885066615585951","title":"Customization of a Severity of Illness Score Using Local Electronic Medical Record Data","year":2015,"lang":"en","type":"article","venue":"Journal of Intensive Care Medicine","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; University of Waterloo","funders":"","keywords":"Medicine; Electronic medical record; Medical record; Personalization; Severity of illness; Emergency medicine; Intensive care medicine; Medical emergency; Internal medicine; World Wide Web","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":[],"consensus_categories":[],"category_scores_codex":[0.007506263,0.0009240866,0.0006488517,0.003183105,0.0001699615,0.001245815,0.0007572652,0.0002713248,0.00119217],"category_scores_gemma":[0.02755272,0.0002049389,0.000834378,0.001961962,0.0002749774,0.0008591028,0.0009726477,0.0004930022,0.0005645663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004735455,"about_ca_system_score_gemma":0.0008458183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00277416,"about_ca_topic_score_gemma":0.003811141,"domain_scores_codex":[0.9960642,0.001458404,0.0008006467,0.0008010338,0.0007541447,0.0001215301],"domain_scores_gemma":[0.9849517,0.005668132,0.003850341,0.002057656,0.003068576,0.0004036506],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005308919,0.0003235278,0.8278533,0.0002653803,0.0004358203,0.000112951,0.0002096198,0.009619452,0.00307944,0.0001816617,0.002451248,0.1549367],"study_design_scores_gemma":[0.0001382678,0.001105958,0.9074381,0.0001274357,0.0002450811,0.0004338663,0.0002648367,0.07936768,0.007162498,0.0004542619,0.003188124,0.00007386696],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9257523,0.0003172048,0.05846116,0.0003223249,0.00008869024,0.001787521,0.008145182,0.001983602,0.003141964],"genre_scores_gemma":[0.9392153,0.000179314,0.05022781,0.0001029282,0.0000744249,0.0005992909,0.009082599,0.00008781574,0.0004305447],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007506263,"threshold_uncertainty_score":0.03969741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1721868585111824,"score_gpt":0.4009913662165814,"score_spread":0.228804507705399,"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."}}