{"id":"W4406598710","doi":"10.4293/jsls.2024.00040","title":"Predicting Robotic Hysterectomy Incision Time: Optimizing Surgical Scheduling with Machine Learning","year":2024,"lang":"en","type":"article","venue":"JSLS Journal of the Society of Laparoscopic & Robotic Surgeons","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; York University; National Institutes of Health; National Science Foundation","keywords":"Hysterectomy; Computer science; Medicine; Artificial intelligence; Surgery","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002399714,0.0006502316,0.0005867693,0.0008997014,0.0002051475,0.0008134018,0.0006961271,0.0005178803,0.0008820506],"category_scores_gemma":[0.006078559,0.0003354878,0.0007071196,0.0005022972,0.0001784783,0.0005570225,0.0004392247,0.0008262981,0.0002534532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008827283,"about_ca_system_score_gemma":0.001592183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01071949,"about_ca_topic_score_gemma":0.007612664,"domain_scores_codex":[0.9994203,0.0002747799,0.00003904268,0.0001319851,0.00005533816,0.00007859919],"domain_scores_gemma":[0.9971041,0.001962992,0.0004562113,0.00007023093,0.000259403,0.0001470513],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003766399,0.0005510729,0.2339954,0.00008501287,0.0002202651,0.00007027465,0.00006149753,0.6895711,0.0005888566,0.0003105537,0.00192602,0.0722433],"study_design_scores_gemma":[0.00001230543,0.00008080834,0.008132493,0.00001206814,0.00002023596,0.00001289423,0.00001645916,0.991069,0.0001946151,0.0003142262,0.0001288283,0.000005967051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9134232,0.0008579371,0.08208738,0.001306941,0.00007025311,0.0001126443,0.0006357272,0.0004945798,0.001011471],"genre_scores_gemma":[0.9820461,0.000104695,0.01684034,0.00008522664,0.00004516276,0.00004101318,0.0004934151,0.00001665333,0.0003274245],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01071949,"threshold_uncertainty_score":0.02131414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02131976514441775,"score_gpt":0.2832383679783957,"score_spread":0.2619186028339779,"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."}}