{"id":"W4319723652","doi":"10.1016/j.ebiom.2023.104464","title":"Predicting survival and neurological outcome in out-of-hospital cardiac arrest using machine learning: the SCARS model","year":2023,"lang":"en","type":"article","venue":"EBioMedicine","topic":"Cardiac Arrest and Resuscitation","field":"Medicine","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Duke Clinical Research Institute; St. Jude Medical; National Institutes of Health; HLS Therapeutics; Hjärt-Lungfonden; Eisai; Belvoir Media Group; Bristol-Myers Squibb; AstraZeneca; CSL Behring; Carl Bennet AB; Amarin Corporation; Boston Scientific Corporation; Idorsia Pharmaceuticals; Cleveland Clinic; Daiichi Sankyo Europe; Sanofi; Amgen; Wallenberg Centre for Molecular and Translational Medicine; Vetenskapsrådet; Pfizer; Ironwood Pharmaceuticals, Incorporated","keywords":"Medicine; Receiver operating characteristic; Population; Emergency medicine; Extracorporeal cardiopulmonary resuscitation; Cardiopulmonary resuscitation; Emergency department; Internal medicine; Resuscitation","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001004676,0.0001568555,0.0004781046,0.0002054381,0.00009601555,0.000008660882,0.00006896369,0.0001001394,0.000003596968],"category_scores_gemma":[0.0004699665,0.00009678922,0.0001080204,0.000545628,0.0002865689,0.00004602952,0.0001111348,0.000431432,0.000004751405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002352286,"about_ca_system_score_gemma":0.00003916568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001440564,"about_ca_topic_score_gemma":0.00001645219,"domain_scores_codex":[0.9985212,0.0001140496,0.0003972606,0.0002695128,0.0004132523,0.0002847263],"domain_scores_gemma":[0.9992524,0.0002904519,0.0001067023,0.0001875176,0.00005450216,0.0001084097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000101041,0.00003432384,0.9921222,0.00007447055,0.00004566753,0.00005975115,0.001677104,0.001695552,0.003164566,0.00003634793,0.00005564744,0.0009332796],"study_design_scores_gemma":[0.00128002,0.0002433051,0.6656928,0.00008659373,0.0001243576,0.000001752733,0.0008113726,0.33129,0.00001871653,0.00006508038,0.0002867244,0.00009928924],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990954,0.0002947531,0.00009353893,0.002954951,0.004976803,0.0003660946,0.00001232349,0.00009014345,0.0002574134],"genre_scores_gemma":[0.998876,0.0001357555,0.0001061691,0.00005207719,0.0006484035,0.000007876593,0.00005842195,0.00002388456,0.00009143481],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3295944,"threshold_uncertainty_score":0.394695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.037614656764846,"score_gpt":0.3118289628197903,"score_spread":0.2742143060549443,"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."}}