{"id":"W2469595319","doi":"10.1017/cem.2016.199","title":"P023: Code Resus - using a quality improvement approach to improve health care provider response during resuscitations","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Emergency Medicine","topic":"Disaster Response and Management","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University Health Network","funders":"","keywords":"Medicine; Emergency department; Health care; CLARITY; Rapid response team; Medical emergency; Cardiopulmonary resuscitation; Quality management; Intervention (counseling); Quality (philosophy); Nursing; Test (biology); Family medicine; Resuscitation; Emergency medicine; Operations management","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":[0.007285176,0.0005180597,0.0003314486,0.001778943,0.004003787,0.00323287,0.002307223,0.003920136,0.05646764],"category_scores_gemma":[0.04288762,0.0003441554,0.0008475871,0.001208491,0.001464656,0.001517212,0.00419679,0.003136942,0.01046247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007577514,"about_ca_system_score_gemma":0.0365679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04208359,"about_ca_topic_score_gemma":0.109171,"domain_scores_codex":[0.9905074,0.002782428,0.0005425945,0.0003849968,0.004549202,0.001233432],"domain_scores_gemma":[0.9446185,0.009291551,0.003605121,0.002386894,0.02718774,0.01291018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0003951575,0.0006226701,0.01623083,0.0004209218,0.00002768389,0.0003386753,0.0009648358,0.0004248125,0.001257765,0.005650942,0.7264384,0.2472272],"study_design_scores_gemma":[0.000920064,0.001411402,0.09466489,0.001556167,0.00008377994,0.001123838,0.002420377,0.004907549,0.004584521,0.00603133,0.8821223,0.0001737854],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.06646419,0.001372332,0.02474534,0.4714445,0.02305791,0.005767445,0.00831016,0.007938092,0.3909001],"genre_scores_gemma":[0.4680878,0.001972873,0.07296084,0.1685329,0.008289935,0.005554578,0.0074897,0.001888254,0.2652231],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05646764,"threshold_uncertainty_score":0.1889031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.214303523592117,"score_gpt":0.4871868916072188,"score_spread":0.2728833680151018,"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."}}