{"id":"W4385074009","doi":"10.5489/cuaj.8265","title":"Development and use of machine learning models for prediction of male sling success","year":2023,"lang":"en","type":"article","venue":"Canadian Urological Association Journal","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"","keywords":"Medicine; Receiver operating characteristic; Urinary incontinence; Logistic regression; Decision tree; Artificial urinary sphincter; Prostatectomy; Random forest; Machine learning; Surgery; Computer science; Internal medicine; Prostate","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.006948808,0.001460088,0.0008990583,0.002781555,0.0003659569,0.001449925,0.001003893,0.0008699985,0.001351807],"category_scores_gemma":[0.01996187,0.0004142287,0.001133636,0.001097951,0.0002410868,0.00100768,0.0005962359,0.001232936,0.0007055229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009098927,"about_ca_system_score_gemma":0.001428082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007545752,"about_ca_topic_score_gemma":0.004872189,"domain_scores_codex":[0.998193,0.0009873462,0.0001543065,0.0002894253,0.0002523558,0.0001235308],"domain_scores_gemma":[0.9836379,0.01321135,0.001077271,0.0003215409,0.001504136,0.0002477936],"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":[0.0005807379,0.0006402481,0.3147593,0.0002483456,0.0007435916,0.0002712243,0.0001874781,0.4652979,0.0009769025,0.0009892741,0.004777023,0.210528],"study_design_scores_gemma":[0.0000181418,0.0001651592,0.008650665,0.00005889187,0.00006696364,0.00008063864,0.00003644759,0.9891094,0.0003429381,0.001013454,0.0004377698,0.00001955335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6343024,0.003053949,0.3502631,0.002118189,0.0002250991,0.0005275606,0.003533974,0.002346352,0.003629457],"genre_scores_gemma":[0.9275606,0.0005670796,0.06876721,0.0001220425,0.0001004055,0.000286679,0.001854926,0.00004494124,0.0006960423],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007545752,"threshold_uncertainty_score":0.0367493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05402267488356309,"score_gpt":0.2545241072427625,"score_spread":0.2005014323591994,"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."}}