{"id":"W4255656020","doi":"10.3410/f.718454802.793496388","title":"Faculty Opinions recommendation of Quantitative metabolomic profiling of serum, plasma, and urine by (1)H NMR spectroscopy discriminates between patients with inflammatory bowel disease and healthy individuals.","year":2014,"lang":"en","type":"dataset","venue":"Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Mount Sinai Hospital","funders":"","keywords":"Urine; Metabolomics; Inflammatory bowel disease; Medicine; Disease; Profiling (computer programming); Internal medicine; Gastroenterology; Chromatography; Chemistry; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001624365,0.002305914,0.001536444,0.002788157,0.0005841383,0.002102235,0.002645865,0.002527048,0.04710017],"category_scores_gemma":[0.008420483,0.0006708346,0.001785235,0.003647454,0.0003589434,0.001060042,0.001703213,0.001575547,0.04066545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001137139,"about_ca_system_score_gemma":0.002833046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01714177,"about_ca_topic_score_gemma":0.04199157,"domain_scores_codex":[0.9988802,0.0001857854,0.0001225484,0.0003782745,0.0002856001,0.0001476483],"domain_scores_gemma":[0.9971145,0.0007540038,0.0004248457,0.0006487898,0.000638582,0.000419333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0003055756,0.00007048576,0.005836497,0.001256726,0.0002274818,0.00004913548,0.00001734353,0.0005133476,0.0004343623,0.0003124864,0.9845198,0.006456641],"study_design_scores_gemma":[0.001173307,0.00007767534,0.03097893,0.0007162967,0.0003454689,0.0001823092,0.00006899541,0.002009542,0.001655502,0.001744077,0.9609627,0.00008537788],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000410133,0.00009126492,0.0001402817,0.0001254246,0.00002719391,0.00001952088,0.9982016,0.000444087,0.0005403992],"genre_scores_gemma":[0.001139118,0.00008696127,0.0005238187,0.00009718482,0.00001153775,0.00007110648,0.9972938,0.0000652348,0.0007111791],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04710017,"threshold_uncertainty_score":0.1575657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01413696813308471,"score_gpt":0.3124012724917071,"score_spread":0.2982643043586224,"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."}}