{"id":"W4394694852","doi":"10.1101/2024.04.09.24305561","title":"Temporal Heart Rhythm Clusters and Physiomorphic Age Mapping: A Deep Learning Approach to Cardiovascular Risk Stratification","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University; Case Western Reserve University; National Heart, Lung, and Blood Institute; University of California, Davis; University of Minnesota; Innovationsfonden; University of Washington; Johns Hopkins University","keywords":"Risk stratification; Stratification (seeds); Rhythm; Heart Rhythm; Geography; Medicine; Internal medicine; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000958716,0.0003376809,0.0008390274,0.0003626636,0.0001372702,0.0001443845,0.0001046816,0.0002434621,0.000004010366],"category_scores_gemma":[0.0001374739,0.0003046295,0.0005879121,0.0004213908,0.00006374475,0.00002268907,0.0003515804,0.001449435,0.00005252298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008319561,"about_ca_system_score_gemma":0.00006155748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004331779,"about_ca_topic_score_gemma":0.000005295167,"domain_scores_codex":[0.9976214,0.000231844,0.0003760081,0.0009689592,0.0005197693,0.0002820347],"domain_scores_gemma":[0.9987131,0.00003210285,0.0001067893,0.0008052981,0.0000893509,0.0002533357],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003054439,0.001229971,0.5151265,0.02490917,0.02651282,0.00165223,0.03049804,0.216386,0.0174617,0.000142979,0.001437379,0.1643378],"study_design_scores_gemma":[0.003354867,0.0008345219,0.3622842,0.00756509,0.01793801,0.0008408891,0.009025043,0.5213633,0.002473295,0.002475498,0.06760409,0.004241223],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9840721,0.00495298,0.008528077,0.0004297635,0.0003496862,0.0005555996,0.000005859144,0.0002411717,0.0008647711],"genre_scores_gemma":[0.9905413,0.0003137647,0.006876025,0.00003143063,0.0008802992,0.0001344016,0.0001434996,0.00006614904,0.001013085],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3049773,"threshold_uncertainty_score":0.9999406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03242197988270906,"score_gpt":0.2654999085176594,"score_spread":0.2330779286349504,"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."}}