{"id":"W3207271987","doi":"10.2196/30798","title":"Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Cardiac Arrest and Resuscitation","field":"Medicine","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Scopus; Data extraction; Systematic review; Medicine; MEDLINE; Artificial intelligence; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008197353,0.00012166,0.0004689742,0.0000778738,0.00004836896,0.0000230801,0.0000760826,0.000156849,0.0002152856],"category_scores_gemma":[0.001576476,0.0001029338,0.0001557108,0.0005543358,0.00008560719,0.0001590626,0.00008617067,0.0004776344,0.0001134794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006398609,"about_ca_system_score_gemma":0.0007365538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004185317,"about_ca_topic_score_gemma":0.00001599718,"domain_scores_codex":[0.9976717,0.00006324407,0.001017726,0.0001082445,0.0008687623,0.0002703657],"domain_scores_gemma":[0.9990241,0.0001973494,0.0001252456,0.0002491597,0.0001310514,0.0002730724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0001015971,0.0004596619,0.03411138,0.09032974,0.000183879,0.0007741849,0.0121191,0.00004939208,0.0001324131,0.003036649,0.006306071,0.8523959],"study_design_scores_gemma":[0.001106185,0.0002181289,0.0192027,0.9088361,0.0004780852,0.0001667981,0.01628429,0.03897571,0.004183415,0.001558548,0.0077537,0.001236279],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7215667,0.0744796,0.02344059,0.025201,0.02561479,0.01101577,0.00004352211,0.0008994807,0.1177385],"genre_scores_gemma":[0.853905,0.09566138,0.01637688,0.02530711,0.006589074,0.0008965302,0.0009173774,0.0001224423,0.0002242463],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8511596,"threshold_uncertainty_score":0.4197519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02864684780833541,"score_gpt":0.3589846247366947,"score_spread":0.3303377769283593,"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."}}