{"id":"W4289731377","doi":"10.1093/jamia/ocac122","title":"Toward ECG-based analysis of hypertrophic cardiomyopathy: a novel ECG segmentation method for handling abnormalities","year":2022,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"QRS complex; Segmentation; Hypertrophic cardiomyopathy; Artificial intelligence; Medicine; Atrial fibrillation; Cardiology; Repolarization; Internal medicine; Electrocardiography; Pattern recognition (psychology); ST segment; Computer science; Electrophysiology; Myocardial infarction","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":[0.0008288745,0.001023609,0.0007701622,0.00238471,0.0004135664,0.0009690717,0.0008770111,0.00131241,0.002052241],"category_scores_gemma":[0.002154609,0.0003007826,0.0009521142,0.001167041,0.0004258267,0.000620192,0.0009496717,0.0007032134,0.002103642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002897462,"about_ca_system_score_gemma":0.0008061773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002583013,"about_ca_topic_score_gemma":0.004006648,"domain_scores_codex":[0.9993641,0.00008283137,0.00005296154,0.0002083455,0.0002336719,0.00005812046],"domain_scores_gemma":[0.9990101,0.0002611107,0.0001364951,0.0001239518,0.0003942248,0.00007413879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004008937,0.0001784427,0.01056152,0.000267676,0.0001715479,0.0006759494,0.0002549702,0.0193774,0.2136332,0.00162894,0.009467515,0.7433819],"study_design_scores_gemma":[0.00009866279,0.0002170572,0.02112126,0.00009766494,0.0001585671,0.002107489,0.0001488459,0.8687702,0.08882044,0.003520192,0.0148651,0.00007453511],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04092013,0.0007739755,0.9504101,0.0002523001,0.00009945973,0.0001465245,0.0004342793,0.005408431,0.001554773],"genre_scores_gemma":[0.1933553,0.0005431168,0.798604,0.0003065239,0.0001828364,0.0001654449,0.001933204,0.0006424196,0.004267134],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002583013,"threshold_uncertainty_score":0.006865442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02240530179786304,"score_gpt":0.3214183110499285,"score_spread":0.2990130092520654,"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."}}