{"id":"W2807316315","doi":"10.1111/petr.13226","title":"Non‐invasive staging of chronic kidney allograft damage using urine metabolomic profiling","year":2018,"lang":"en","type":"article","venue":"Pediatric Transplantation","topic":"Renal Transplantation Outcomes and Treatments","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Children's Hospital; The Metabolomics Innovation Centre; University of Alberta; Manitoba Health; University of Manitoba; University of British Columbia; Children's Hospital of Winnipeg; Children's Hospital Research Institute of Manitoba; McMaster University","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; BC Children's Hospital","keywords":"Medicine; Metabolite; Urine; Internal medicine; Metabolomics; Kidney transplantation; Proteinuria; Confounding; Urinary system; Kidney; Kidney disease; Metabolome; Creatinine; Urology; Gastroenterology; Bioinformatics; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.0001264164,0.0002025025,0.0003342157,0.0003135789,0.00009561289,0.00001226247,0.00007301466,0.0000790739,0.00009733457],"category_scores_gemma":[0.00001411627,0.0001761196,0.0001325763,0.0004000086,0.00006259601,0.0001851596,0.000005423779,0.0001122277,0.00002265123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006881964,"about_ca_system_score_gemma":0.000350878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001270857,"about_ca_topic_score_gemma":0.00001897844,"domain_scores_codex":[0.9986616,0.00003214234,0.0004491438,0.0002885758,0.0002982443,0.0002703198],"domain_scores_gemma":[0.9992419,0.00006950353,0.0001940786,0.0001754504,0.0001556828,0.0001633759],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005963419,0.0002244938,0.9203932,0.003956651,0.0002553682,0.0001290572,0.0007973063,0.0002145785,0.07313377,0.00008677427,0.00000722498,0.0002052005],"study_design_scores_gemma":[0.007956211,0.0007431046,0.6733852,0.000409148,0.005178727,0.0001483427,0.00003822214,0.003235974,0.3085396,0.00004336116,0.0000171387,0.0003049829],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9856915,0.000214843,0.01259918,0.00005030811,0.0002474569,0.0005249038,0.000157041,0.00005491834,0.0004598214],"genre_scores_gemma":[0.9846257,0.003662719,0.01045665,0.00009817447,0.0005229994,0.00001372901,0.0005076901,0.0000313306,0.00008101563],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.247008,"threshold_uncertainty_score":0.7181949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01950750542000191,"score_gpt":0.2952390920615068,"score_spread":0.2757315866415049,"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."}}