{"id":"W2941363586","doi":"10.1097/01.ju.0000555241.27498.f6","title":"PD07-08 MACHINE LEARNING TO PREDICT RECURRENCE OF LOCALIZED RENAL CELL CARCINOMA","year":2019,"lang":"en","type":"article","venue":"The Journal of Urology","topic":"Renal cell carcinoma treatment","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Nomogram; Renal cell carcinoma; Stage (stratigraphy); T-stage; Artificial intelligence; Radiology; Machine learning; Oncology; Cancer; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003010973,0.0004748045,0.0006378609,0.002351655,0.0003135767,0.001875548,0.0009090157,0.0008587046,0.01958323],"category_scores_gemma":[0.01266684,0.0002392214,0.0007759757,0.00131658,0.000191357,0.0006403088,0.001069374,0.00123923,0.01403674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007311016,"about_ca_system_score_gemma":0.001391539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003932961,"about_ca_topic_score_gemma":0.005149781,"domain_scores_codex":[0.9986799,0.0004398337,0.0001002084,0.0001566766,0.0005132041,0.0001101126],"domain_scores_gemma":[0.9948487,0.002014819,0.0003716669,0.0003529309,0.001931851,0.00048008],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003765654,0.0003474798,0.07779126,0.0002497275,0.0001780137,0.0001106583,0.00002193659,0.006319745,0.0004096808,0.00111674,0.5500798,0.3629984],"study_design_scores_gemma":[0.0006069379,0.00198089,0.1708695,0.001710111,0.0004298126,0.00204464,0.0002017066,0.4320747,0.006314018,0.00991494,0.3736849,0.0001678074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3098015,0.03364932,0.1536892,0.06665167,0.01227215,0.002368701,0.1823193,0.02379417,0.2154541],"genre_scores_gemma":[0.5762705,0.01133691,0.1087374,0.006077701,0.00508127,0.0015129,0.1923397,0.001842285,0.09680137],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01958323,"threshold_uncertainty_score":0.06551242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01473007743056672,"score_gpt":0.2442395615276871,"score_spread":0.2295094840971204,"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."}}