{"id":"W2992878763","doi":"10.1177/0840470419872770","title":"Further reducing the rate of code blue calls through early warning systems and enabling technologies","year":2019,"lang":"en","type":"article","venue":"Healthcare Management Forum","topic":"Healthcare Technology and Patient Monitoring","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro One (Canada)","funders":"","keywords":"Harm; Warning system; Burnout; Business; Health care; Quality (philosophy); Healthcare system; Medical emergency; Analytics; Medicine; Internet privacy; Public relations; Risk analysis (engineering); Computer science; Psychology; Data science; Telecommunications; Political science","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":[0.01184158,0.0008454411,0.0003902566,0.001648633,0.001779252,0.005914749,0.001310731,0.004120529,0.01288533],"category_scores_gemma":[0.05852585,0.0002468449,0.0009534404,0.000817063,0.001315885,0.00738355,0.003708223,0.00404816,0.002942906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00135453,"about_ca_system_score_gemma":0.004463803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002400323,"about_ca_topic_score_gemma":0.002223091,"domain_scores_codex":[0.9907046,0.004742019,0.00046476,0.0004575932,0.002441926,0.001189125],"domain_scores_gemma":[0.958911,0.02381781,0.003702507,0.002514225,0.00815534,0.002899182],"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":[0.0003799762,0.001123394,0.0316541,0.001668804,0.000143267,0.001444755,0.004630904,0.003364068,0.008927152,0.03987671,0.2044736,0.7023132],"study_design_scores_gemma":[0.0003710995,0.002790172,0.03926369,0.004469027,0.0004180596,0.003332762,0.01109479,0.01610117,0.0280547,0.06576325,0.827949,0.0003922804],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1173397,0.01158686,0.1244256,0.6422712,0.009999461,0.0006726494,0.0007246925,0.00461866,0.08836108],"genre_scores_gemma":[0.8183689,0.01339661,0.08008379,0.06158009,0.005835096,0.0004370487,0.0005551141,0.0005330539,0.01921031],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01288533,"threshold_uncertainty_score":0.06262499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03132270995133966,"score_gpt":0.3056596108622704,"score_spread":0.2743369009109307,"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."}}