{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006208602,0.001071781,0.0009514096,0.001684516,0.0003277542,0.000773811,0.001148406,0.0007447419,0.001767793],"category_scores_gemma":[0.01035784,0.0002634774,0.001232652,0.0007100418,0.0005580707,0.0005632943,0.000787191,0.001053993,0.000491994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006018755,"about_ca_system_score_gemma":0.001231117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008668771,"about_ca_topic_score_gemma":0.004470281,"domain_scores_codex":[0.998921,0.0006085401,0.00005257988,0.0001702865,0.0001323115,0.000115259],"domain_scores_gemma":[0.9931235,0.005350047,0.0004494629,0.0002459094,0.0005817686,0.0002494619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0007883746,0.0005143489,0.2214988,0.00010752,0.0004419071,0.0003366775,0.0001586048,0.6996096,0.0003597575,0.001648544,0.004580148,0.06995571],"study_design_scores_gemma":[0.00004009674,0.0001220612,0.008829073,0.00002905234,0.00004610487,0.0000567395,0.00002941866,0.9882051,0.0001190907,0.002306949,0.0002062336,0.00001004336],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9030731,0.0009373821,0.08841823,0.002419571,0.0001447995,0.0001747793,0.001433266,0.0005238839,0.002875018],"genre_scores_gemma":[0.9890144,0.0002030162,0.008498308,0.000141416,0.00008020617,0.00008360915,0.001076742,0.00001770987,0.0008844401],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008668771,"threshold_uncertainty_score":0.03283459,"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."}}