{"id":"W2095878770","doi":"10.25011/cim.v31i4.4824","title":"USE OF AN ELECTRONIC DATA WAREHOUSE TO ENHANCE CARDIAC SURGICAL SITE SURVEILLANCE AT A LARGE CANADIAN CENTRE","year":2008,"lang":"en","type":"article","venue":"Clinical and investigative medicine","topic":"Surgical site infection prevention","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Surgical site infection; Electronic data; Logistic regression; Electronic surveillance; Emergency medicine; Medical emergency; Surgery; Computer science; Internal medicine; Database","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.04172099,0.0006317259,0.001684222,0.01962491,0.001043523,0.003285798,0.002105736,0.0006750929,0.001642193],"category_scores_gemma":[0.1302974,0.0005872532,0.002312019,0.0238568,0.000525411,0.00221478,0.001619895,0.0005710789,0.0002120568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0168466,"about_ca_system_score_gemma":0.05846164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3978982,"about_ca_topic_score_gemma":0.5860353,"domain_scores_codex":[0.9667745,0.01308587,0.007188446,0.001704515,0.01062198,0.0006247244],"domain_scores_gemma":[0.827892,0.0821588,0.02516455,0.006242959,0.05607574,0.002465877],"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":[0.0008228387,0.000404197,0.2035562,0.08683714,0.004514557,0.0003697661,0.002867746,0.002167029,0.001043805,0.001241318,0.01849371,0.6776817],"study_design_scores_gemma":[0.002562055,0.001849236,0.7363933,0.1134374,0.01789548,0.0006596583,0.006157835,0.01194624,0.004628771,0.001947564,0.1018433,0.0006792442],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4656795,0.285208,0.04734779,0.02925401,0.0009575303,0.02496235,0.111058,0.002436792,0.03309602],"genre_scores_gemma":[0.6309559,0.07048232,0.2571863,0.003504289,0.0002236881,0.009117894,0.02730817,0.0001020654,0.001119394],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3978982,"threshold_uncertainty_score":0.7911644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1544706291045937,"score_gpt":0.3731086311854938,"score_spread":0.2186380020809001,"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."}}