{"id":"W3162729225","doi":"10.1002/hep.31907","title":"Admission Urinary and Serum Metabolites Predict Renal Outcomes in Hospitalized Patients With Cirrhosis","year":2021,"lang":"en","type":"article","venue":"Hepatology","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Alberta","funders":"National Center for Advancing Translational Sciences; National Institute on Aging; Agency for Healthcare Research and Quality; Grifols; National Institute of Diabetes and Digestive and Kidney Diseases; Mallinckrodt Pharmaceuticals; U.S. Department of Veterans Affairs","keywords":"Medicine; Cirrhosis; Acute kidney injury; Creatinine; Dialysis; Urinary system; Urine; Cohort; Internal medicine; Intensive care medicine; Cohort study; Nephrology; Gastroenterology; Urology","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":[0.00007142701,0.0001654052,0.0003585588,0.00006766697,0.00005521265,0.00001074535,0.00007123029,0.000119292,0.00004592622],"category_scores_gemma":[0.0001385194,0.0001277457,0.0000480683,0.00011688,0.00007294243,0.000004426159,0.0001990296,0.00007918262,0.000001373881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007274491,"about_ca_system_score_gemma":0.00005732396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001692853,"about_ca_topic_score_gemma":0.00006201267,"domain_scores_codex":[0.9989678,0.00008762189,0.0001948585,0.0004013136,0.00008857342,0.0002597898],"domain_scores_gemma":[0.9995271,0.00001758451,0.00006543111,0.0002328867,0.00008020151,0.00007677282],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00010348,0.0001916929,0.9906243,0.00001378896,0.0001226051,0.00002723312,0.00002019508,2.512719e-7,0.008113842,0.0002031428,0.0001799493,0.0003995206],"study_design_scores_gemma":[0.00177142,0.0004033547,0.9666366,0.000007606548,0.00004250924,0.00002163329,0.0000250672,0.00001251352,0.01743975,0.0001659636,0.01329939,0.0001741938],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952866,0.00352917,0.00003384459,0.0004557464,0.0001050578,0.000120808,0.00004386947,0.000008885365,0.0004160422],"genre_scores_gemma":[0.9968403,0.001376008,0.001050985,0.0002694884,0.00002401155,0.00002559836,0.00009782404,0.00001523417,0.0003005338],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0239877,"threshold_uncertainty_score":0.520932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004853865527573458,"score_gpt":0.2207857067429818,"score_spread":0.2159318412154083,"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."}}