{"id":"W4313547826","doi":"10.1016/j.jpi.2022.100177","title":"An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS)","year":2022,"lang":"en","type":"review","venue":"Journal of Pathology Informatics","topic":"AI in cancer detection","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Cancer Institute","keywords":"Genitourinary system; Standardization; Computer science; Digital pathology; Grading (engineering); Pathology; Surgical pathology; Deep learning; Anatomical pathology; Artificial intelligence; Data science; Medical physics; Bioinformatics; Medicine; Biology; Anatomy","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.007284091,0.001043511,0.003334797,0.0004076477,0.0008931785,0.0002675341,0.004419048,0.001121438,0.0001827106],"category_scores_gemma":[0.001739293,0.000766049,0.001936517,0.0007973973,0.0004759507,0.003073952,0.001146618,0.004012764,0.0001021246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000730611,"about_ca_system_score_gemma":0.001887579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000138287,"about_ca_topic_score_gemma":0.000001234986,"domain_scores_codex":[0.9885461,0.00406561,0.004611652,0.0007306573,0.001082661,0.0009633521],"domain_scores_gemma":[0.9801391,0.008131785,0.008561202,0.002057791,0.0008479695,0.0002620872],"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":[0.0001048071,0.0004485178,0.000002009383,0.004429337,0.0004932441,0.005368502,0.002565546,0.000677641,0.000002590099,0.001243906,0.0316152,0.9530487],"study_design_scores_gemma":[0.000883033,0.003413243,0.00001904669,0.001995079,0.001518695,0.09397665,0.0005265715,0.0009158896,6.877803e-7,0.003059702,0.8930117,0.0006797102],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00007993614,0.8245178,0.1651081,0.002895182,0.004677748,0.001908298,0.0005266403,0.0001098233,0.0001764497],"genre_scores_gemma":[0.000008717956,0.8087453,0.1700116,0.01904184,0.001278355,0.0004219772,0.0003952647,0.00008395265,0.00001299039],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.952369,"threshold_uncertainty_score":0.9994791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06624757497684777,"score_gpt":0.370564328847515,"score_spread":0.3043167538706673,"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."}}