{"id":"W4368228275","doi":"10.32604/iasc.2023.038338","title":"Machine Learning Prediction Models of Optimal Time for Aortic Valve Replacement in Asymptomatic Patients","year":2023,"lang":"en","type":"article","venue":"Intelligent Automation & Soft Computing","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval; Université Laval","keywords":"Aortic valve replacement; Computer science; Support vector machine; Decision tree; CHAID; Medicine; Stenosis; Asymptomatic; Artificial intelligence; Surgery; Internal medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.0003606828,0.000159911,0.0003329188,0.0003020084,0.00007969242,0.00001818399,0.00004954757,0.00005648717,0.00002794557],"category_scores_gemma":[0.0002365049,0.000156919,0.0004904025,0.0003645445,0.00001710719,0.00009685011,0.00005970095,0.00008559499,0.00005959582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002012699,"about_ca_system_score_gemma":0.00004642392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002543527,"about_ca_topic_score_gemma":2.995313e-7,"domain_scores_codex":[0.9983853,0.00005489355,0.0006791045,0.0002699201,0.0003571596,0.0002536335],"domain_scores_gemma":[0.9990884,0.0002246736,0.0002606476,0.0001621905,0.0001863665,0.00007772708],"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":[0.0002026796,0.0004824356,0.4868041,0.0003934364,0.0005252613,9.30771e-7,0.001662081,0.4705668,0.00009316273,0.00005496197,0.0001148842,0.03909926],"study_design_scores_gemma":[0.001275841,0.0003453053,0.1811491,0.0003407605,0.0002030964,8.893986e-7,0.0001014361,0.8161353,0.0002229325,0.0001264256,0.0000162239,0.00008267035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9654078,0.00008407275,0.03266851,0.00002818434,0.0001589661,0.001133082,0.00004261415,0.0003320682,0.000144677],"genre_scores_gemma":[0.9974318,0.00001516512,0.001278988,0.0000219601,0.00003958434,0.0000471798,0.001002902,0.00003047882,0.000131923],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3455684,"threshold_uncertainty_score":0.6398969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01848982581673441,"score_gpt":0.312732152241804,"score_spread":0.2942423264250696,"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."}}