{"id":"W2154637023","doi":"10.1109/iembs.2009.5332509","title":"Optimizing cardiac resuscitation outcomes using wavelet analysis","year":2009,"lang":"en","type":"article","venue":"","topic":"Cardiac Arrest and Resuscitation","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; St. Michael's Hospital; Toronto General Hospital","funders":"Canadian Institutes of Health Research","keywords":"Defibrillation; Ventricular fibrillation; Medicine; Return of spontaneous circulation; Shock (circulatory); Cardiopulmonary resuscitation; Fibrillation; Resuscitation; Cardiology; Internal medicine; Automated external defibrillator; Sudden cardiac death; Anesthesia; Atrial fibrillation","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008333002,0.0003922674,0.0004232982,0.0007907139,0.00009328834,0.0005447636,0.0002104161,0.0002588975,0.0005770818],"category_scores_gemma":[0.003121085,0.0001205833,0.0002116951,0.0004077313,0.00009275593,0.0006309448,0.0003101817,0.0003161791,0.0003044043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001382329,"about_ca_system_score_gemma":0.0002204203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004041188,"about_ca_topic_score_gemma":0.0004280993,"domain_scores_codex":[0.9997664,0.00009084875,0.00001894168,0.00004566677,0.00005697289,0.00002114668],"domain_scores_gemma":[0.9995618,0.0001861564,0.00006884151,0.00003442205,0.0001235959,0.00002513064],"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":[0.0006828394,0.000334498,0.04035065,0.0001346947,0.0001094701,0.0001974031,0.0001819363,0.05998891,0.04978576,0.0009835893,0.001405775,0.8458444],"study_design_scores_gemma":[0.00005913466,0.0008519989,0.04641101,0.00004446553,0.00009965798,0.000320575,0.0002126789,0.9239987,0.02421321,0.002441505,0.00130064,0.00004645137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5328277,0.0004956471,0.4637804,0.0002235707,0.00004058838,0.0001167776,0.0001891558,0.0006392725,0.001686909],"genre_scores_gemma":[0.9014761,0.0003542023,0.09715864,0.00002665551,0.00002505042,0.00005306448,0.0002553969,0.00004478041,0.000605996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008333002,"threshold_uncertainty_score":0.004406989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02185451009334071,"score_gpt":0.3160822980540093,"score_spread":0.2942277879606686,"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."}}