{"id":"W4392127977","doi":"10.1016/j.resplu.2024.100587","title":"Prediction of outcomes after cardiac arrest by a generative artificial intelligence model","year":2024,"lang":"en","type":"article","venue":"Resuscitation Plus","topic":"Cardiac Arrest and Resuscitation","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Gottfried und Julia Bangerter-Rhyner-Stiftung; Universität Basel; Mach-Gaensslen Foundation of Canada; Society of General Internal Medicine; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Generative grammar; Transformer; Artificial intelligence; Generative model; Computer science; Outcome (game theory); Medicine; Machine learning; Internal medicine; Engineering; Mathematics","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":[0.001613197,0.000613747,0.0004523952,0.001014506,0.0001850805,0.0009599631,0.0006981099,0.0005441549,0.001126796],"category_scores_gemma":[0.004698414,0.0002412689,0.0009516263,0.0003586615,0.0004884094,0.0003670239,0.0006316695,0.0007798505,0.0002890131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006695674,"about_ca_system_score_gemma":0.0005487532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005018183,"about_ca_topic_score_gemma":0.003900004,"domain_scores_codex":[0.9996079,0.0001794191,0.00001928599,0.00009693391,0.00004710316,0.00004947501],"domain_scores_gemma":[0.9980666,0.001436219,0.0001923442,0.0001070001,0.0001194999,0.00007831925],"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":[0.0007452481,0.0003422253,0.3271443,0.00008731453,0.0006036922,0.0006915694,0.0003022602,0.6054612,0.002489378,0.00324786,0.00164142,0.05724349],"study_design_scores_gemma":[0.00001965517,0.0001087925,0.01684092,0.00001299372,0.0000628336,0.0001483374,0.00002907247,0.9803784,0.0002292487,0.002036905,0.0001194657,0.00001334265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9011208,0.0002740418,0.09497406,0.000662216,0.00004532191,0.00008528832,0.000696109,0.0004092801,0.001732858],"genre_scores_gemma":[0.9955089,0.00004842587,0.003652171,0.00004549372,0.00001142595,0.00002353274,0.0003587576,0.000007047452,0.0003442984],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005018183,"threshold_uncertainty_score":0.009977937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03146207616398493,"score_gpt":0.3002970821449762,"score_spread":0.2688350059809912,"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."}}