{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009799823,0.0001227673,0.0001633831,0.0001813962,0.0002157323,0.00002739393,0.00007462995,0.00003158647,0.000007288762],"category_scores_gemma":[0.0001252046,0.00009557336,0.00001728006,0.0002495735,0.00005284282,0.00005828873,0.00003234646,0.00008510116,4.592106e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002235604,"about_ca_system_score_gemma":0.0006591392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002934361,"about_ca_topic_score_gemma":0.0003156181,"domain_scores_codex":[0.9987215,0.0002152903,0.0003798283,0.000335513,0.0002028986,0.0001449099],"domain_scores_gemma":[0.9990884,0.0000610415,0.0002215346,0.0001530323,0.0004054941,0.0000704741],"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":[0.001014839,0.0003837117,0.01615713,0.0003633671,0.0003538873,0.000002346866,0.00608214,0.02759694,0.02615315,0.001020706,0.0001060701,0.9207657],"study_design_scores_gemma":[0.001250978,0.0005441432,0.02836899,0.0002278495,0.0005336487,0.000004560848,0.0008420698,0.9417984,0.02601651,0.0001600672,0.0001197929,0.0001329871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8795912,0.0003106312,0.1160425,0.001324415,0.0003232333,0.002207085,0.00005201151,0.00003424691,0.000114701],"genre_scores_gemma":[0.9917035,0.00001422567,0.00759572,0.0003265559,0.00009908753,0.0001199238,0.00009579195,0.00002311312,0.00002214273],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9206327,"threshold_uncertainty_score":0.3897369,"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."}}