{"id":"W4288056712","doi":"10.1016/j.mlwa.2022.100387","title":"ABC: Artificial Intelligence for Bladder Cancer grading system","year":2022,"lang":"en","type":"article","venue":"Machine Learning with Applications","topic":"Bladder and Urothelial Cancer Treatments","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sinai Health System; Toronto General Hospital; Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Grading (engineering); Computer science; Bladder cancer; Artificial intelligence; Deep learning; Residual neural network; Artificial neural network; Medical physics; Cancer; Medicine; Engineering","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":[0.0006861133,0.0006576116,0.0005354512,0.001678161,0.0003835906,0.001045306,0.001082412,0.0007900572,0.004942537],"category_scores_gemma":[0.002721759,0.0002199496,0.000559636,0.001155754,0.0001809391,0.0007379168,0.001073417,0.0008036106,0.002586327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008911493,"about_ca_system_score_gemma":0.001210866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007847222,"about_ca_topic_score_gemma":0.006629719,"domain_scores_codex":[0.9993993,0.00009141749,0.0000848276,0.0001294276,0.0002324852,0.00006253352],"domain_scores_gemma":[0.9994295,0.00008570155,0.00005105564,0.00006505792,0.0003275903,0.00004092077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004068659,0.0001820664,0.01379854,0.0006667273,0.0001689254,0.0003004906,0.0001228708,0.04590632,0.01278038,0.005014048,0.1264968,0.794156],"study_design_scores_gemma":[0.00008044687,0.0002602365,0.01300575,0.0001823938,0.0001173182,0.0005236028,0.0000990193,0.8755642,0.02186934,0.01140512,0.07679196,0.0001007144],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.101733,0.004453198,0.7665809,0.002538792,0.001129138,0.001584183,0.02513088,0.06674416,0.03010576],"genre_scores_gemma":[0.5060304,0.002118814,0.42243,0.0009851198,0.0002281182,0.001695096,0.04978933,0.0008798429,0.01584329],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007847222,"threshold_uncertainty_score":0.01653445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02822631841757555,"score_gpt":0.311564374460185,"score_spread":0.2833380560426095,"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."}}