{"id":"W4239280028","doi":"10.32920/ryerson.14665728.v1","title":"Morphologically constrained adaptive signal decompositions in studying ventricular arrhythmias","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":"Pattern recognition (psychology); Ventricular fibrillation; Sudden cardiac death; SIGNAL (programming language); Artificial intelligence; Classifier (UML); Linear discriminant analysis; Discriminant; Cardiac arrhythmia; Hilbert–Huang transform; Cardiology; Computer science; Internal medicine; Medicine; Atrial fibrillation","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":[0.0006865587,0.0005567669,0.0003845746,0.001015069,0.0001699612,0.0006302221,0.0002767417,0.0005488116,0.0006909102],"category_scores_gemma":[0.001760438,0.0001664059,0.0005439101,0.00103078,0.0004667408,0.0005884198,0.0004395368,0.0005663834,0.0003138435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001820193,"about_ca_system_score_gemma":0.0002846436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001130634,"about_ca_topic_score_gemma":0.0009306038,"domain_scores_codex":[0.9997687,0.00007463559,0.00001620139,0.00006008828,0.00005968336,0.00002069417],"domain_scores_gemma":[0.9996145,0.0001919171,0.00005594732,0.00004107842,0.00007344913,0.00002312202],"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":[0.0002896236,0.0001601975,0.005388393,0.000318106,0.00009928524,0.0004596506,0.0003843225,0.2886134,0.2118914,0.01394811,0.001024157,0.4774233],"study_design_scores_gemma":[0.000007382059,0.0001278332,0.004799048,0.00002362079,0.000024088,0.0001376775,0.00009115891,0.9797813,0.009625684,0.003862741,0.001500324,0.00001912198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08886285,0.000730239,0.9084785,0.0001125124,0.00005793515,0.00008349014,0.00007642002,0.000175232,0.001422691],"genre_scores_gemma":[0.5877926,0.001380314,0.4083478,0.00007345017,0.00008991806,0.00014529,0.0002488919,0.00006591136,0.001855907],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001130634,"threshold_uncertainty_score":0.003630877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04476802529016404,"score_gpt":0.3004653621614135,"score_spread":0.2556973368712495,"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."}}