{"id":"W3184856471","doi":"10.2196/30770","title":"Prediction of Critical Care Outcome for Adult Patients Presenting to Emergency Department Using Initial Triage Information: An XGBoost Algorithm Analysis","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Emergency and Acute Care Studies","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Triage; Emergency department; Receiver operating characteristic; Medicine; Machine learning; Logistic regression; Respiratory rate; Artificial intelligence; Vital signs; Discriminative model; Blood pressure; Algorithm; Emergency medicine; Computer science; Heart rate; Internal medicine; Surgery","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.003060251,0.0008903667,0.001371661,0.001336696,0.000525892,0.0006967235,0.0008997078,0.001068705,0.001437467],"category_scores_gemma":[0.003304897,0.0003117393,0.001032172,0.0006541275,0.000287209,0.0004340115,0.0004941461,0.0009505937,0.0002905135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000770967,"about_ca_system_score_gemma":0.001963096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008792185,"about_ca_topic_score_gemma":0.003373501,"domain_scores_codex":[0.9993332,0.000262342,0.00005789877,0.0001517229,0.00009203311,0.0001026665],"domain_scores_gemma":[0.9987587,0.0007126544,0.0001006997,0.00004177353,0.0002977829,0.0000884457],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001338121,0.001414273,0.124767,0.0001446346,0.000529247,0.0002507554,0.0001234175,0.6612694,0.001738936,0.0007117391,0.003063561,0.204649],"study_design_scores_gemma":[0.00002707696,0.000157279,0.003844285,0.00001098214,0.0000330991,0.00002315659,0.00001234673,0.9953561,0.0002230996,0.0001795982,0.0001287376,0.000004315328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8079309,0.001276519,0.186816,0.0008322034,0.0001593386,0.0003637284,0.0003631407,0.0007159443,0.001542226],"genre_scores_gemma":[0.9468991,0.0002734205,0.05038324,0.0001931289,0.00006470975,0.0002730937,0.0007439404,0.00003212426,0.001137404],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008792185,"threshold_uncertainty_score":0.01748204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05036555010433651,"score_gpt":0.3965936565845112,"score_spread":0.3462281064801747,"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."}}