{"id":"W4392807872","doi":"10.1016/j.urolonc.2024.01.162","title":"DEVELOPMENT AND EXTERNAL VALIDATION OF NIMBLE, AN ARTIFICIAL INTELLIGENCE-BASED TOOL TO PREDICT PROGRESSION IN NON-MUSCLE INVASIVE BLADDER CANCER: A RETROSPECTIVE MULTI-INSTITUTIONAL COHORT STUDY","year":2024,"lang":"en","type":"article","venue":"Urologic Oncology Seminars and Original Investigations","topic":"Bladder and Urothelial Cancer Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Public Health Ontario; Sinai Health System; Mount Sinai Hospital; University of Toronto; University Health Network; Trillium Health Centre","funders":"","keywords":"Bladder cancer; Retrospective cohort study; Cancer; Medicine; Cohort; Artificial intelligence; Computer science; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[0.008454284,0.0008617435,0.0006854113,0.001697616,0.001061418,0.0017358,0.001357173,0.001235586,0.001171404],"category_scores_gemma":[0.01611154,0.0005378338,0.001040126,0.001117593,0.001046841,0.001105936,0.001843572,0.0009636502,0.0007619954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007582559,"about_ca_system_score_gemma":0.001500823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00363693,"about_ca_topic_score_gemma":0.005004219,"domain_scores_codex":[0.9969705,0.001125573,0.0003481649,0.0007483966,0.0005712559,0.0002360091],"domain_scores_gemma":[0.9908014,0.002136333,0.001526383,0.002564359,0.002376956,0.0005946213],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001871549,0.0007586996,0.9811266,0.00007289861,0.000447741,0.0001310575,0.0007497022,0.0005885821,0.001926044,0.0001875559,0.0008749097,0.01126481],"study_design_scores_gemma":[0.0002562497,0.001680651,0.9877592,0.0000490727,0.0004522616,0.0005591613,0.0006447813,0.003171737,0.001515405,0.0002502957,0.003619454,0.00004179488],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9943942,0.0001487495,0.002589467,0.00004590764,0.00003315527,0.0004106136,0.00159106,0.00003362222,0.0007532656],"genre_scores_gemma":[0.9896877,0.0001152887,0.003541398,0.00007757678,0.00003437469,0.0006755731,0.005013247,0.00004664661,0.0008082241],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.008454284,"threshold_uncertainty_score":0.04471105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04661617954541949,"score_gpt":0.3640919495551017,"score_spread":0.3174757700096822,"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."}}