{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001431922,0.0002422181,0.0008387025,0.0001040419,0.0002481707,0.00007546746,0.0001926642,0.0001493578,0.0002653987],"category_scores_gemma":[0.0003060522,0.0001433369,0.0009711831,0.0006381202,0.0001776635,0.0001887086,0.00009722822,0.001168328,0.000007951756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001268042,"about_ca_system_score_gemma":0.0002960024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002542034,"about_ca_topic_score_gemma":0.000001679491,"domain_scores_codex":[0.9974781,0.0002091067,0.0008802268,0.0002369025,0.0008339199,0.0003618035],"domain_scores_gemma":[0.9973904,0.001486261,0.0004610449,0.0002151337,0.0002358704,0.0002112259],"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":[0.0004392875,0.0002056642,0.1504293,0.0005575029,0.001394938,0.0002009025,0.002162691,0.8393677,0.001914078,0.00006886593,0.00009408901,0.003164981],"study_design_scores_gemma":[0.008405974,0.000820003,0.01205561,0.01252914,0.001614059,0.002292319,0.001408674,0.9569453,0.001620742,0.00007677509,0.001830099,0.0004012911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9875779,0.002906418,0.005787501,0.001785029,0.0007625113,0.0002552424,0.000001419123,0.00009242758,0.0008315298],"genre_scores_gemma":[0.9908866,0.0001229235,0.007872207,0.00009262181,0.0002073034,0.000001581821,0.000003974141,0.00004385889,0.0007689497],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1383737,"threshold_uncertainty_score":0.5845109,"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."}}