{"id":"W2978257108","doi":"10.1039/c9an01642b","title":"Metabolomics for improved treatment monitoring of phenylketonuria: urinary biomarkers for non-invasive assessment of dietary adherence and nutritional deficiencies","year":2019,"lang":"en","type":"article","venue":"The Analyst","topic":"Metabolism and Genetic Disorders","field":"Biochemistry, Genetics and Molecular Biology","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; McMaster University; Genome Canada","keywords":"Urinary system; Metabolomics; Medicine; Phenylketonurias; Intensive care medicine; Internal medicine; Bioinformatics; Biology; Phenylalanine; Biochemistry; Amino acid","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001205959,0.0007440014,0.0008646148,0.00154452,0.0002596601,0.001113211,0.0003108199,0.0007575062,0.0007660599],"category_scores_gemma":[0.001375855,0.0002104835,0.0003914265,0.00122284,0.0002430569,0.0004228507,0.0005134323,0.0006729776,0.000264921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003290234,"about_ca_system_score_gemma":0.0004216314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001158916,"about_ca_topic_score_gemma":0.002100006,"domain_scores_codex":[0.9994783,0.0001809926,0.00004522175,0.0001320389,0.0001265634,0.00003687451],"domain_scores_gemma":[0.9993047,0.0001833541,0.0002732154,0.0000370069,0.0001398133,0.00006197608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001852756,0.0004894702,0.4330133,0.001314893,0.000808072,0.0008092473,0.0004103742,0.001418371,0.306135,0.0006174598,0.003470568,0.2496604],"study_design_scores_gemma":[0.0001121914,0.001808843,0.7945735,0.0005416146,0.000769526,0.004966181,0.000595391,0.02298583,0.1567006,0.001987553,0.01480485,0.0001538606],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8897759,0.0537405,0.04470886,0.001848848,0.0002638217,0.0001925348,0.00459457,0.0009284038,0.003946515],"genre_scores_gemma":[0.9476849,0.01089339,0.03700593,0.0009171686,0.000207249,0.0001635815,0.00145449,0.00007142688,0.001601729],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00154452,"threshold_uncertainty_score":0.006377816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0190557450164665,"score_gpt":0.2931571739077279,"score_spread":0.2741014288912614,"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."}}