{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004102137,0.0006627852,0.001315555,0.0006022192,0.005062628,0.004212216,0.001354755,0.04769969,0.00793701],"category_scores_gemma":[0.02890304,0.0005149463,0.001200156,0.0004581598,0.003527529,0.006735458,0.002995363,0.04398106,0.00374114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004525619,"about_ca_system_score_gemma":0.005875381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004982912,"about_ca_topic_score_gemma":0.01098548,"domain_scores_codex":[0.9958029,0.001817789,0.0004570442,0.0003700325,0.001013708,0.0005386119],"domain_scores_gemma":[0.9878604,0.007012411,0.0006213359,0.0002751202,0.0014653,0.002765438],"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":[0.00004144093,0.0001061696,0.001334552,0.0001017666,0.00002846624,0.002303954,0.0003116193,0.0001241944,0.0001802658,0.006517672,0.9645475,0.02440236],"study_design_scores_gemma":[0.0001851822,0.0001827826,0.002033795,0.0009654578,0.0000627659,0.005685222,0.001197341,0.0009626013,0.0002485398,0.04288131,0.9454792,0.0001158395],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"editorial","genre_scores_codex":[0.0001520844,0.0009458634,0.00009185068,0.9928247,0.004866854,0.000003585771,0.000009886119,0.000009069219,0.001096127],"genre_scores_gemma":[0.003887078,0.001844657,0.0004050784,0.9594985,0.03098764,0.00001868036,0.00001311998,0.00001404386,0.003331231],"genre_candidate":"editorial","genre_consensus":null,"teacher_disagreement_score":0.04769969,"threshold_uncertainty_score":0.03283578,"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."}}