{"id":"W4381193117","doi":"10.1088/1361-6579/acdfb4","title":"A unified framework for multi-lead ECG characterization using Laplacian Eigenmaps","year":2023,"lang":"en","type":"article","venue":"Physiological Measurement","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Libin Cardiovascular Institute of Alberta; University of Calgary","funders":"Vlaamse regering; Fonds Wetenschappelijk Onderzoek; European Space Agency","keywords":"Heartbeat; Pattern recognition (psychology); Normal Sinus Rhythm; Artificial intelligence; Lead (geology); Representation (politics); Sinus rhythm; Laplace operator; Computer science; Electrocardiography; Dimensionality reduction; Curse of dimensionality; Set (abstract data type); Cardiology; Medicine; Mathematics; Atrial fibrillation; Biology","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":[0.0003036286,0.0001471631,0.0003212557,0.00008386894,0.0001806636,0.00001297433,0.00007148675,0.0001239582,0.00001428251],"category_scores_gemma":[0.0004905829,0.0001094034,0.0001763312,0.0003954408,0.00003461073,0.00003009637,0.00002970958,0.0001450171,0.00006017907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001124937,"about_ca_system_score_gemma":0.00003857227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000792131,"about_ca_topic_score_gemma":5.404139e-7,"domain_scores_codex":[0.9987593,0.00005539791,0.000228695,0.0003082749,0.0003488657,0.0002994299],"domain_scores_gemma":[0.9993283,0.00003675889,0.00008450912,0.0002098846,0.0002359587,0.0001046063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001180657,0.000238325,0.003445128,0.00008174204,0.000130227,0.000007340244,0.00008943372,0.0000805238,0.9875879,0.00009436913,0.00006272578,0.008064207],"study_design_scores_gemma":[0.003265212,0.001214849,0.5914957,0.001052289,0.0006228074,0.000009166066,0.0004632967,0.2944094,0.1025406,0.00204546,0.002038778,0.0008424612],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8919227,0.00004352806,0.106704,0.0003185747,0.0002890187,0.0004335999,0.00001289161,0.000257424,0.00001820736],"genre_scores_gemma":[0.9756975,0.00007513123,0.02320772,0.0001625575,0.0005495781,0.00007545249,0.0001128845,0.0000186477,0.0001005336],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8850473,"threshold_uncertainty_score":0.446134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.406600383780414,"score_gpt":0.3954386581023632,"score_spread":0.01116172567805074,"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."}}