{"id":"W4367367470","doi":"10.1038/s41416-023-02289-9","title":"Precision medicine for prostate cancer—improved outcome prediction for low-intermediate risk disease using a six-gene copy number alteration classifier","year":2023,"lang":"en","type":"editorial","venue":"British Journal of Cancer","topic":"Prostate Cancer Treatment and Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Prostate cancer; Classifier (UML); Medicine; Oncology; Cohort; Internal medicine; Pathology; Artificial intelligence; Computer science; Cancer","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":[0.01320973,0.002784635,0.003350575,0.0029695,0.001360069,0.004937503,0.00324936,0.01032202,0.006322016],"category_scores_gemma":[0.03824709,0.001405926,0.003086046,0.001196152,0.002198439,0.003304391,0.001016465,0.01875464,0.005435775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002559875,"about_ca_system_score_gemma":0.002336766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002898098,"about_ca_topic_score_gemma":0.003671719,"domain_scores_codex":[0.9943678,0.001581324,0.0008970294,0.0007109757,0.002261658,0.000181232],"domain_scores_gemma":[0.9598575,0.02271706,0.001043378,0.0006664777,0.01379349,0.0019221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003814546,0.00002963693,0.0001225571,0.0007518741,0.0002068844,0.0001346598,0.00002112996,0.0001078402,0.0002097213,0.0005507019,0.9715149,0.0259686],"study_design_scores_gemma":[0.001040112,0.0003565607,0.001880415,0.002154965,0.0009139505,0.001034167,0.00007272129,0.002281762,0.001076519,0.004646683,0.984426,0.0001160145],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.0002172963,0.03111491,0.001203511,0.05980159,0.9059609,0.00007109177,0.0002728911,0.0001883894,0.001169361],"genre_scores_gemma":[0.001821408,0.01974607,0.0008944927,0.03228866,0.9375157,0.00007740639,0.0001730364,0.0000929106,0.007390372],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.01320973,"threshold_uncertainty_score":0.06986058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03510274844559041,"score_gpt":0.3993405678416556,"score_spread":0.3642378193960651,"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."}}