{"id":"W4377087086","doi":"10.1016/s2589-7500(23)00067-5","title":"Development, multi-institutional external validation, and algorithmic audit of an artificial intelligence-based Side-specific Extra-Prostatic Extension Risk Assessment tool (SEPERA) for patients undergoing radical prostatectomy: a retrospective cohort study","year":2023,"lang":"en","type":"article","venue":"The Lancet Digital Health","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Public Health Ontario; Trillium Health Centre; Sinai Health System; Mount Sinai Hospital; Princess Margaret Cancer Centre; University Health Network; University of Toronto; Queen's University","funders":"University of Toronto","keywords":"Nomogram; Prostatectomy; Receiver operating characteristic; Medicine; Logistic regression; Audit; Retrospective cohort study; Prostate cancer; Cohort; Artificial intelligence; Machine learning; Cancer; Surgery; Oncology; Internal medicine; Computer science; Accounting","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009705809,0.0002382979,0.0005896359,0.0001212663,0.0004448746,0.0001118943,0.00009315628,0.00003995237,0.000006154451],"category_scores_gemma":[0.0002063021,0.0001627106,0.00006062234,0.0002907378,0.000159595,0.0002402108,0.00004769077,0.0001846172,0.000006371715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006895064,"about_ca_system_score_gemma":0.001038279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003085942,"about_ca_topic_score_gemma":0.0000428051,"domain_scores_codex":[0.9976978,0.0001012791,0.0006787566,0.0005323872,0.0005675786,0.000422241],"domain_scores_gemma":[0.9985971,0.0003272162,0.0003303906,0.00030773,0.0002787064,0.0001588526],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008629137,0.002694697,0.7911911,0.0002563581,0.0001466666,0.0000189311,0.001351267,0.0002408753,0.00003379196,0.0004321027,0.00005607076,0.2027152],"study_design_scores_gemma":[0.004117804,0.003006723,0.9822588,0.0003662136,0.00004898261,0.000005624131,0.0004329917,0.006596248,0.0006888116,0.002207851,0.000100181,0.0001697604],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9739679,0.00009705096,0.01950046,0.0004083476,0.0001725804,0.005230646,0.0005207812,0.00009191105,0.00001031964],"genre_scores_gemma":[0.9845558,0.0003431884,0.0133964,0.0001123108,0.0001243387,0.0007315716,0.0006982876,0.00002842714,0.000009627467],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2025455,"threshold_uncertainty_score":0.6635147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06582539565123856,"score_gpt":0.3569413291739577,"score_spread":0.2911159335227191,"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."}}