{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001258373,0.0001326394,0.0001874543,0.00008640083,0.0008312684,0.00002179956,0.00009946925,0.00002271731,0.0002886512],"category_scores_gemma":[0.000008407008,0.0001121001,0.00006281539,0.0003423863,0.00003685919,0.0000253778,0.00003684285,0.0002901946,0.00001807678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000230503,"about_ca_system_score_gemma":0.0001263274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003867118,"about_ca_topic_score_gemma":0.00004269846,"domain_scores_codex":[0.9990357,0.00003235424,0.0001917278,0.0003113147,0.0002121799,0.0002167302],"domain_scores_gemma":[0.9994743,0.00006289426,0.0000944125,0.0002117929,0.00006595996,0.0000906131],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003272126,0.001766796,0.1461218,0.001154841,0.00189323,0.00004532958,0.00413682,0.07969062,0.005690899,0.1742563,0.001820058,0.5801512],"study_design_scores_gemma":[0.003544103,0.004179099,0.006852668,0.0002175228,0.002486623,0.0005892388,0.006718321,0.1703474,0.006082918,0.003145372,0.7945383,0.001298496],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0367224,0.003227309,0.9316488,0.008390412,0.0003220836,0.008174506,0.0005573807,0.001165278,0.009791857],"genre_scores_gemma":[0.9841498,0.00001233629,0.003250471,0.0002183142,0.0002245543,0.01050182,0.0001667608,0.00004728763,0.001428714],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9474273,"threshold_uncertainty_score":0.6393528,"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."}}