{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002617583,0.0002581913,0.0007395286,0.0002732636,0.00006361213,0.00005574009,0.0000966548,0.0002938715,0.0007240905],"category_scores_gemma":[0.00008104377,0.0002200099,0.000378858,0.000329659,0.00006798493,0.00002349042,0.0002759281,0.0009664585,0.00001783031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001898674,"about_ca_system_score_gemma":0.0002685258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002235913,"about_ca_topic_score_gemma":0.00002514339,"domain_scores_codex":[0.9982406,0.0001329494,0.0004578362,0.0005806142,0.000311497,0.0002764786],"domain_scores_gemma":[0.9990488,0.000130741,0.0001179471,0.0003450026,0.0002041761,0.0001532878],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009976384,0.01074124,0.3932671,0.001385086,0.01214035,0.3466157,0.003765255,0.1233018,0.05196283,0.001433339,0.001011065,0.05337867],"study_design_scores_gemma":[0.02414267,0.003755941,0.5822307,0.02345299,0.0132463,0.008832034,0.05453164,0.2410315,0.03655478,0.004565863,0.0004724306,0.007183188],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9679086,0.0007401143,0.02507032,0.0008933798,0.0001796171,0.0003944171,0.0000129099,0.000116832,0.00468382],"genre_scores_gemma":[0.9693919,0.00007383065,0.02938249,0.000158442,0.0003050473,0.00006932668,0.0001737214,0.00001634914,0.0004288988],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3377837,"threshold_uncertainty_score":0.8971743,"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."}}