{"id":"W4387709906","doi":"10.1016/j.jacc.2023.09.395","title":"TCT-387 TAVI-PREP: A Deep Learning-Based Tool for Automated Measurements Extraction in TAVR Planning","year":2023,"lang":"en","type":"article","venue":"Journal of the American College of Cardiology","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Montreal Heart Institute","funders":"","keywords":"Medicine; Extraction (chemistry); Medical physics; Chromatography","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":[0.0003951593,0.001577928,0.0005900451,0.001194686,0.0004290207,0.001062086,0.001300708,0.001088979,0.01858212],"category_scores_gemma":[0.00215413,0.000771072,0.0008224898,0.0005991583,0.000228295,0.0005894029,0.001122695,0.001315914,0.004584199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000596708,"about_ca_system_score_gemma":0.001629037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009129725,"about_ca_topic_score_gemma":0.01794981,"domain_scores_codex":[0.9998036,0.00002101933,0.00001885949,0.00007071881,0.00005750758,0.00002832625],"domain_scores_gemma":[0.9995481,0.0002078359,0.00005172522,0.00006133765,0.0000891407,0.00004179662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008659863,0.0002366558,0.006737414,0.0005461968,0.0002589371,0.000772568,0.0001518232,0.09358908,0.01854796,0.005237337,0.1090913,0.7639648],"study_design_scores_gemma":[0.00006194125,0.0001129496,0.001955325,0.00006815071,0.00003746186,0.0003261742,0.0000309037,0.9513093,0.02464988,0.00582408,0.01557522,0.00004862487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01383056,0.000646657,0.8765619,0.0003038881,0.0001319049,0.0001628281,0.006451094,0.0988436,0.003067557],"genre_scores_gemma":[0.2547581,0.0006109346,0.7136661,0.0006730335,0.00009584685,0.0004597928,0.01439869,0.005251104,0.01008643],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01858212,"threshold_uncertainty_score":0.06216335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01858028592093154,"score_gpt":0.27895057499904,"score_spread":0.2603702890781084,"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."}}