{"id":"W7115710809","doi":"10.2196/78484","title":"Early Prediction of Cardiac Arrest Based on Time-Series Vital Signs Using Deep Learning: Retrospective Study","year":2025,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Vital signs; Retrospective cohort study; Signs and symptoms; Generalization; Sensitivity (control systems); Health care","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001438287,0.0003857511,0.0004476857,0.0009282743,0.0001848135,0.0005180282,0.0005516922,0.0004535347,0.0007286887],"category_scores_gemma":[0.004315446,0.0002343777,0.0005981495,0.0004974908,0.0002690207,0.0004575023,0.0004408115,0.0006630648,0.0003365119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003150609,"about_ca_system_score_gemma":0.0004977842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004315356,"about_ca_topic_score_gemma":0.004382553,"domain_scores_codex":[0.9995346,0.000106497,0.00006510114,0.0001525063,0.00008651388,0.00005484021],"domain_scores_gemma":[0.9976882,0.0007531595,0.000422833,0.0003749232,0.0005921688,0.0001687547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005277465,0.0001804285,0.9759435,0.000056309,0.0002023376,0.0005975435,0.00008248481,0.003049443,0.0008355678,0.0001262574,0.001119131,0.01727935],"study_design_scores_gemma":[0.0000661317,0.00117513,0.9001713,0.0001154798,0.0004612369,0.002542854,0.0004657688,0.0881495,0.002643296,0.0005663154,0.003570257,0.00007283033],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931611,0.0005408819,0.004212171,0.00007101023,0.00001690503,0.00004536241,0.0015469,0.0000277348,0.0003779385],"genre_scores_gemma":[0.9958926,0.0002693139,0.0009835416,0.00003338989,0.00002194315,0.00002824067,0.002595281,0.000007422795,0.00016817],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004315356,"threshold_uncertainty_score":0.008580446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03461208290013593,"score_gpt":0.3812115022774255,"score_spread":0.3465994193772896,"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."}}