{"id":"W4415824661","doi":"10.2196/76021","title":"Predicting Postoperative Stress Urinary Incontinence After Prolapse Surgery via Machine Learning and Regression Models: Development and Validation Study","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Pelvic floor disorders treatments","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Urinary incontinence; Support vector machine; Stress incontinence; Predictive modelling; Predictive value of tests; Regression; Clinical prediction rule","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.01000863,0.001137087,0.0007279675,0.0008540677,0.0002898509,0.0004922689,0.0008479588,0.0006360724,0.0008613314],"category_scores_gemma":[0.01257375,0.0003399962,0.00122697,0.0006405813,0.0003098179,0.0006088121,0.000581352,0.0009426065,0.0003770801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005158128,"about_ca_system_score_gemma":0.001254491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005408331,"about_ca_topic_score_gemma":0.003168534,"domain_scores_codex":[0.997965,0.001203755,0.0001413754,0.0002436991,0.0003226266,0.0001236242],"domain_scores_gemma":[0.9903656,0.005882109,0.0005618459,0.0007073082,0.002291057,0.000191963],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002260081,0.005852952,0.4499165,0.0004712002,0.001291868,0.0003908785,0.0004040429,0.2439102,0.004686916,0.0006757882,0.003054087,0.2870855],"study_design_scores_gemma":[0.0001803477,0.003149458,0.06821132,0.0001291898,0.0003592822,0.0002026069,0.0001457,0.9227105,0.003574999,0.0002262418,0.001072679,0.0000378302],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9725633,0.0009614025,0.02453512,0.0001228352,0.00005520472,0.0003437396,0.0004218387,0.0001779938,0.0008186605],"genre_scores_gemma":[0.9759227,0.0004747065,0.02142038,0.00004028523,0.00002077811,0.0002751114,0.001283427,0.00001939493,0.0005431814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01000863,"threshold_uncertainty_score":0.05293137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01375182966151681,"score_gpt":0.2924145559644652,"score_spread":0.2786627263029484,"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."}}