{"id":"W4401732982","doi":"10.1145/3670085.3670091","title":"Predicting Mitral Valve mTEER Surgery Outcomes Using Machine Learning and Deep Learning Techniques","year":2024,"lang":"en","type":"article","venue":"","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; St. Michael's Hospital; Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Mitral valve; Machine learning; Medicine; Internal medicine","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":[0.002019974,0.001302732,0.0008290989,0.001380112,0.0002468913,0.000955732,0.0008628339,0.0009491195,0.000735625],"category_scores_gemma":[0.007133002,0.0002205422,0.0008387264,0.0007416769,0.0003133463,0.000678701,0.0009779003,0.001530161,0.0004027464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005994016,"about_ca_system_score_gemma":0.000996499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004240159,"about_ca_topic_score_gemma":0.00597025,"domain_scores_codex":[0.9989122,0.0003828217,0.0001297664,0.0002660282,0.0001727915,0.0001363235],"domain_scores_gemma":[0.9972909,0.001666524,0.0002713354,0.000245851,0.0002987708,0.0002266227],"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":[0.001287508,0.001877978,0.2868018,0.0006092282,0.0007982427,0.0005191253,0.0001365149,0.3050818,0.003450239,0.001479313,0.02109489,0.3768635],"study_design_scores_gemma":[0.0001062061,0.0008179896,0.02674582,0.0001141147,0.0001295983,0.0002643858,0.0001114446,0.9618373,0.003752716,0.00323258,0.002842281,0.000045504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9048892,0.004829636,0.07120046,0.002707163,0.0003754965,0.0002242828,0.01138751,0.001306112,0.003080134],"genre_scores_gemma":[0.9435961,0.0008846888,0.0363467,0.0004264336,0.0001801653,0.0001242744,0.01756015,0.00005225683,0.0008292146],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004240159,"threshold_uncertainty_score":0.01068276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01830865086927211,"score_gpt":0.3448055825180941,"score_spread":0.326496931648822,"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."}}