{"id":"W4391317825","doi":"10.1161/circep.123.012338","title":"Artificial Intelligence Model Predicts Sudden Cardiac Arrest Manifesting With Pulseless Electric Activity Versus Ventricular Fibrillation","year":2024,"lang":"en","type":"article","venue":"Circulation Arrhythmia and Electrophysiology","topic":"Cardiac Arrest and Resuscitation","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Heart, Lung, and Blood Institute","keywords":"Medicine; Pulseless electrical activity; Ventricular fibrillation; Cohort; Receiver operating characteristic; Defibrillation; Internal medicine; Cardiology; Sudden cardiac arrest; Logistic regression; Cardiopulmonary resuscitation; Emergency medicine; Resuscitation","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":[0.0001241702,0.0002032722,0.0003241697,0.0002117702,0.0001755939,0.00005632388,0.00002719016,0.0001606836,0.000006973678],"category_scores_gemma":[0.00006541954,0.0001726989,0.0001148221,0.0005902206,0.00006838585,0.0001804205,0.0000166332,0.0002691167,0.00002607035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001443698,"about_ca_system_score_gemma":0.0002304433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001346252,"about_ca_topic_score_gemma":0.00000230185,"domain_scores_codex":[0.9986288,0.00008020571,0.0002222617,0.0004849248,0.0002308025,0.0003530107],"domain_scores_gemma":[0.9993681,0.0001615662,0.00007170871,0.0001735949,0.0001124056,0.0001126489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003381848,0.00003697523,0.002156941,0.0005182938,0.0005978333,0.0004332157,0.0004606119,0.1452578,0.6080893,0.01211241,0.00004995138,0.2269048],"study_design_scores_gemma":[0.0002618381,0.0004296475,0.04889219,0.0001298989,0.0003878723,0.0001245244,0.00003768763,0.9390803,0.007208164,0.002982557,0.0001377715,0.0003275478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9676552,0.0007878717,0.02920482,0.0003274876,0.001112488,0.0004294109,0.000004038816,0.0001448431,0.0003337814],"genre_scores_gemma":[0.9976417,0.0001935784,0.0002107649,0.00001397023,0.001775286,0.00002551799,0.00008902034,0.00003177584,0.00001837138],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7938225,"threshold_uncertainty_score":0.7042457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01857197169039587,"score_gpt":0.2679593731177276,"score_spread":0.2493874014273317,"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."}}