{"id":"W4414905242","doi":"10.1016/j.eururo.2025.09.4145","title":"Development and International Evaluation of an Artificial Intelligence–based Model (PROGRxN-BCa) Using the World Health Organization 2004/2022 Grading System to Predict Progression Risk and Improve Substratification for Non–muscle-invasive Bladder Cancer","year":2025,"lang":"en","type":"article","venue":"European Urology","topic":"Bladder and Urothelial Cancer Treatments","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Vector Institute; Dalhousie University; Université Laval; Centre Hospitalier de l’Université de Montréal; University Health Network; Ottawa Hospital; University of British Columbia; McGill University Health Centre; Sinai Health System; University of Calgary; Mount Sinai Hospital; University of Alberta; Trillium Health Centre; Université de Sherbrooke; University of Toronto","funders":"Temerty Faculty of Medicine, University of Toronto; University of Toronto; Bladder Cancer Canada","keywords":"Bladder cancer; Risk stratification; Grading (engineering); Cohort; Risk assessment; MEDLINE","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004895145,0.0005695685,0.0004165624,0.0007933571,0.0003701755,0.0009430198,0.000867308,0.000496367,0.001454652],"category_scores_gemma":[0.006522175,0.0001723381,0.0005646077,0.0004548934,0.0002066807,0.0005399722,0.0006454834,0.0005746407,0.0004301168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001299313,"about_ca_system_score_gemma":0.002649385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03295491,"about_ca_topic_score_gemma":0.02259519,"domain_scores_codex":[0.999083,0.0004407763,0.00008767371,0.0001372848,0.0002013986,0.0000497849],"domain_scores_gemma":[0.9974282,0.0007810926,0.0001551819,0.0002072421,0.001313908,0.0001143212],"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":[0.001567024,0.0019505,0.1688502,0.0002025794,0.0008183063,0.000143379,0.0001905927,0.3381868,0.009535789,0.003073808,0.01059984,0.4648812],"study_design_scores_gemma":[0.0001252269,0.0007666599,0.03154862,0.0000431023,0.0002406735,0.00006465208,0.00008441757,0.9566878,0.005907589,0.0008371826,0.003666088,0.00002792166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8041963,0.000964954,0.1751633,0.001542561,0.000314836,0.001117802,0.002883676,0.001056317,0.01276029],"genre_scores_gemma":[0.8989274,0.0002815318,0.0929484,0.0001695132,0.00003159355,0.0003633191,0.003617107,0.00007622471,0.003584819],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.03295491,"threshold_uncertainty_score":0.06552619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05980056407992791,"score_gpt":0.3676882168239772,"score_spread":0.3078876527440493,"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."}}