{"id":"W3040384889","doi":"10.1016/j.cmet.2020.06.005","title":"A Universal Gut-Microbiome-Derived Signature Predicts Cirrhosis","year":2020,"lang":"en","type":"article","venue":"Cell Metabolism","topic":"Liver Disease Diagnosis and Treatment","field":"Medicine","cited_by":336,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia Hospital","funders":"National Center for Advancing Translational Sciences; National Institute of Environmental Health Sciences; Chiba University; University of California, San Diego; National Cancer Institute; National Institutes of Health; National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; U.S. Department of Defense; University of Arizona Cancer Center; Fondation Leducq; U.S. Public Health Service; Howard Hughes Medical Institute","keywords":"Cirrhosis; Microbiome; Metagenomics; Gut microbiome; Biology; Computational biology; Medicine; Gastroenterology; Bioinformatics; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00003184424,0.0002344887,0.0004002551,0.00007208849,0.00006952255,0.00002650311,0.0001258506,0.0001246829,0.000810847],"category_scores_gemma":[0.00002495858,0.0001936327,0.0002305766,0.0002923495,0.00005708371,0.00009252335,0.00007757631,0.0001846473,0.0003692051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003976294,"about_ca_system_score_gemma":0.0001233167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002106631,"about_ca_topic_score_gemma":5.235476e-7,"domain_scores_codex":[0.9988618,0.00004369748,0.000173735,0.000412459,0.0002197067,0.0002886123],"domain_scores_gemma":[0.9989874,0.00003121669,0.00007035572,0.0002635817,0.00009073059,0.000556756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008014438,0.002407248,0.01035244,0.0005585794,0.001389196,0.001401386,0.005925423,0.00002031327,0.8497426,0.0007486705,0.1201302,0.006522492],"study_design_scores_gemma":[0.01523973,0.0005070919,0.204885,0.0001304947,0.004707735,0.00001709433,0.0008980288,0.0002323695,0.3587478,0.00006652784,0.4138732,0.0006949073],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9703429,0.01313914,0.00006439999,0.006217637,0.0004124135,0.0008158127,0.0001867469,0.000261939,0.008558962],"genre_scores_gemma":[0.9934336,0.0006960267,0.0005858822,0.004189992,0.0004275499,0.00002546834,0.0001394874,0.00003929486,0.0004626928],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4909948,"threshold_uncertainty_score":0.8878206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01218734290194393,"score_gpt":0.2098971391217194,"score_spread":0.1977097962197755,"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."}}