{"id":"W4237224670","doi":"10.32920/ryerson.14660661","title":"Ventricular fibrillation detection algorithm for automated external defibrillators","year":2021,"lang":"en","type":"preprint","venue":"","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Ventricular fibrillation; Normal Sinus Rhythm; Defibrillation; Autoregressive model; Algorithm; Computer science; Automated external defibrillator; Sinus rhythm; Cardiology; Atrial fibrillation; Internal medicine; Pattern recognition (psychology); Artificial intelligence; Medicine; Mathematics; Cardiopulmonary resuscitation; Statistics; Resuscitation; Anesthesia","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":[0.0001606186,0.0002331839,0.0005170386,0.0002237545,0.00009075423,0.00009741154,0.00004383271,0.0003692576,0.00003703225],"category_scores_gemma":[0.000085465,0.0002147425,0.0005378017,0.0002175318,0.00001412565,0.00003884546,0.0001032824,0.0002066491,0.000009147282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002006638,"about_ca_system_score_gemma":0.0001245425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002156615,"about_ca_topic_score_gemma":0.000004987055,"domain_scores_codex":[0.9985856,0.0000354136,0.0003612401,0.0004881895,0.0003083082,0.0002212376],"domain_scores_gemma":[0.9989424,0.00004818868,0.0001760992,0.0003648967,0.0003446687,0.0001237943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004292166,0.00006409414,0.01452419,0.0007958606,0.001386317,0.0001403959,0.0000814598,0.007772972,0.009560883,0.000003903621,0.0002814331,0.9653456],"study_design_scores_gemma":[0.0006011434,0.00009145553,0.007941626,0.0005877988,0.0009985183,0.000165878,0.00008279642,0.9212108,0.06700548,0.00005523895,0.0009720093,0.0002872528],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5800197,0.001513088,0.4156996,0.0000640115,0.001164152,0.0004548979,0.00001250813,0.0008475916,0.0002244866],"genre_scores_gemma":[0.8744965,0.0001626558,0.1216509,0.00002696767,0.002228525,0.0000380957,0.0003329649,0.0000519604,0.001011423],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9650583,"threshold_uncertainty_score":0.8756945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0162030629965781,"score_gpt":0.2951024586699217,"score_spread":0.2788993956733436,"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."}}