{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000171951,0.0001073459,0.0002248387,0.00003171765,0.0000476475,0.000006030655,0.0001249837,0.00004626474,0.000003059771],"category_scores_gemma":[0.00001849812,0.00007513162,0.0001506902,0.00005520636,0.00009292685,0.000003203533,0.0000405234,0.00001424476,1.138859e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003522385,"about_ca_system_score_gemma":0.00009335757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003120226,"about_ca_topic_score_gemma":0.000007764281,"domain_scores_codex":[0.9993942,0.00002173083,0.0002005609,0.0001968678,0.00005983081,0.0001267634],"domain_scores_gemma":[0.9994225,0.00007541588,0.0001379415,0.0002346507,0.0001044804,0.00002504231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002284777,0.0001636515,0.01680842,0.0001084649,0.0004684352,4.272195e-8,0.00005996554,0.00006215808,0.980257,0.0001049838,0.00004107074,0.001697361],"study_design_scores_gemma":[0.004149961,0.003517431,0.137048,0.00004354251,0.000861582,0.000003470928,0.002627452,0.002104696,0.8465937,0.0007699223,0.001996465,0.0002837661],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939643,0.003169921,0.001792024,0.00005212582,0.00009820053,0.0006087901,0.0002141324,0.000001361957,0.00009913159],"genre_scores_gemma":[0.9885252,0.001544896,0.009547609,0.00001175764,0.00005427388,0.0001011518,0.00009913473,0.000007896741,0.0001081448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1336632,"threshold_uncertainty_score":0.3063779,"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."}}