{"id":"W2999568707","doi":"10.23919/cinc49843.2019.9005547","title":"Feasibility of Automated Vital Sign Instability Detection in Children Admitted to the Pediatric Intensive Care Unit","year":2019,"lang":"en","type":"article","venue":"Computing in Cardiology Conference","topic":"Cardiac Arrest and Resuscitation","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Vital signs; Pediatric intensive care unit; False positive paradox; Medicine; Neonatal intensive care unit; Intensive care unit; Harm; Medical emergency; Emergency medicine; Intensive care medicine; Pediatrics; Computer science; Machine learning; Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005581168,0.0001301595,0.0004867939,0.0001964571,0.00003260386,0.000006192186,0.0001204389,0.0001549924,0.00000331498],"category_scores_gemma":[0.000947617,0.0001009276,0.0001045208,0.0006062818,0.00008656533,0.00002887249,0.000122426,0.0003551045,0.00001333072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001315548,"about_ca_system_score_gemma":0.0002138747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001515461,"about_ca_topic_score_gemma":0.00008535222,"domain_scores_codex":[0.9985121,0.000408764,0.0003627818,0.0003670535,0.000131207,0.0002180151],"domain_scores_gemma":[0.9984288,0.0002980449,0.00009946851,0.0003734205,0.0007500235,0.00005020772],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001464213,0.00001171141,0.9910856,0.00005358305,0.0000206145,0.000003702771,0.001987116,0.003111543,0.001377934,0.00000541847,0.000005697369,0.002190611],"study_design_scores_gemma":[0.001047255,0.0001334872,0.9918085,0.00005180796,0.00003297312,0.00001694585,0.001963827,0.004617177,0.0002107082,0.0000201281,0.000002727599,0.00009450579],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968436,0.00009949828,0.0006841163,0.00007315087,0.0007271062,0.00125628,0.000008922714,0.00007972293,0.0002275658],"genre_scores_gemma":[0.9997193,0.000005103689,0.00004968425,0.00006835769,0.0001132698,0.000007814187,0.00002760887,0.000007693014,0.000001205172],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002875626,"threshold_uncertainty_score":0.4115708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02389421238129884,"score_gpt":0.3023619879175599,"score_spread":0.278467775536261,"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."}}