{"id":"W1836606401","doi":"10.1016/j.eururo.2015.06.037","title":"Personalized Medicine in Kidney Cancer: Learning How to Walk Before We Run","year":2015,"lang":"en","type":"letter","venue":"European Urology","topic":"Renal cell carcinoma treatment","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"Canadian Institutes of Health Research","keywords":"Medicine; Renal cell carcinoma; Radiogenomics; Clear cell renal cell carcinoma; Kidney cancer; Metastasis; Oncology; Internal medicine; Personalized medicine; Cancer; Gene signature; Pathology; Bioinformatics; Gene expression; Gene; Radiology","routes":{"ca_aff":true,"ca_fund":true,"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","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007364559,0.0006666152,0.001338496,0.0007027807,0.00005860976,0.00001773254,0.0003752585,0.0004468808,0.001120189],"category_scores_gemma":[0.0008805361,0.0005130278,0.0001775531,0.0003481879,0.0002763025,0.00003340623,0.0002256398,0.003193257,0.0008241763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005698527,"about_ca_system_score_gemma":0.0003740627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002914864,"about_ca_topic_score_gemma":0.00004508755,"domain_scores_codex":[0.9956186,0.001288467,0.0004934431,0.001037501,0.0006712335,0.0008906969],"domain_scores_gemma":[0.9981291,0.000127411,0.0002618107,0.0006825861,0.0002000191,0.0005990774],"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":[0.0003642714,0.00002970692,0.003922283,0.0002417782,0.0001816205,0.02578303,0.003105379,0.0000102545,0.0002632998,0.000005804312,0.9624601,0.003632443],"study_design_scores_gemma":[0.004154722,0.003551665,0.005214874,0.0004257391,0.000444811,0.0006720116,0.00008746409,0.00002121842,0.000009824591,0.00001735745,0.985073,0.0003272772],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.01212641,0.004133543,0.00001038207,0.94019,0.0008799201,0.0007929839,0.00002638911,0.0001508069,0.04168958],"genre_scores_gemma":[0.03755413,0.0003807909,0.0001559176,0.8451104,0.01068928,0.0000543136,0.001062086,0.0003483484,0.1046447],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.09507956,"threshold_uncertainty_score":0.9999538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03511328726494337,"score_gpt":0.2735569426563131,"score_spread":0.2384436553913697,"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."}}