{"id":"W4312932119","doi":"10.1016/j.jelectrocard.2022.07.046","title":"Ventricular tachycardia localization from 12 lead ECG data through probabilistic active guidance","year":2022,"lang":"en","type":"article","venue":"Journal of Electrocardiology","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Health Sciences Centre","funders":"","keywords":"Probabilistic logic; Cardiology; Ventricular tachycardia; Medicine; Internal medicine; Lead (geology); Electrocardiography; Tachycardia; Artificial intelligence; Computer science; 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":[0.0004736297,0.0001614014,0.0004751203,0.00009167632,0.0002358398,0.00003404981,0.001208284,0.00006181504,0.0000503481],"category_scores_gemma":[0.0008001122,0.0001394345,0.0001582802,0.0002994014,0.0001076553,0.0003711564,0.0004534367,0.0006061334,0.000008785901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001882103,"about_ca_system_score_gemma":0.0002132453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002289384,"about_ca_topic_score_gemma":0.000003392323,"domain_scores_codex":[0.9972744,0.001036783,0.0004853469,0.0004473312,0.000407632,0.0003485137],"domain_scores_gemma":[0.9983397,0.0005135644,0.0005204402,0.0004923536,0.00008354116,0.00005045588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003082268,0.001026746,0.0101828,0.0000723616,0.001509818,0.009163132,0.002786391,0.259174,0.5392575,0.00476741,0.1455584,0.02341923],"study_design_scores_gemma":[0.003793645,0.006150322,0.003997549,0.00008939197,0.0006045621,0.02204721,0.0005517777,0.06639099,0.3388242,0.04590421,0.5104779,0.001168275],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6957497,0.004626298,0.2916716,0.001771961,0.003596783,0.0005035668,0.0002318623,0.00007890923,0.001769332],"genre_scores_gemma":[0.9971377,0.0001137354,0.0007584838,0.001289693,0.000605309,0.000006769395,0.00002935945,0.00001846795,0.0000404346],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3649195,"threshold_uncertainty_score":0.5685975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0430855913737572,"score_gpt":0.2918274269208593,"score_spread":0.2487418355471021,"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."}}