{"id":"W4246583795","doi":"10.32920/ryerson.14660661.v1","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; Sinus rhythm; Automated external defibrillator; Cardiology; Atrial fibrillation; Internal medicine; Pattern recognition (psychology); Artificial intelligence; Medicine; Mathematics; Cardiopulmonary resuscitation; Statistics; Resuscitation; Surgery","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004820838,0.0003876409,0.0005430049,0.001079039,0.0003099878,0.0007067002,0.0007763965,0.0007130254,0.002642236],"category_scores_gemma":[0.001507421,0.0002341582,0.0003975827,0.0005841762,0.0001382753,0.0004379147,0.000384376,0.0004618937,0.001472091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003954917,"about_ca_system_score_gemma":0.0004891437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001332531,"about_ca_topic_score_gemma":0.00128315,"domain_scores_codex":[0.9995123,0.00007033171,0.00005071071,0.0001300031,0.0001954309,0.00004115826],"domain_scores_gemma":[0.9995186,0.0001479716,0.0000403365,0.00004084519,0.0002376376,0.00001461459],"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":[0.0002961383,0.0001083809,0.002479843,0.00009418172,0.00005631888,0.0001636454,0.00005960355,0.03197418,0.06424982,0.00300088,0.004663196,0.8928538],"study_design_scores_gemma":[0.00008230411,0.0001728344,0.0047891,0.00002598787,0.00003319024,0.000545003,0.00002381942,0.9512492,0.03135844,0.001828557,0.009864069,0.00002750616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02219292,0.0002658699,0.9733975,0.0000880955,0.00006140275,0.00008728407,0.0001134132,0.002353318,0.001440251],"genre_scores_gemma":[0.1945423,0.0002731251,0.7985541,0.0001362122,0.00007969593,0.0002380779,0.0006077173,0.00008669677,0.005482088],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002642236,"threshold_uncertainty_score":0.00883919,"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."}}