{"id":"W3104869804","doi":"10.1002/edm2.201","title":"Metabolic dysfunction in pregnancy: Fingerprinting the maternal metabolome using proton nuclear magnetic resonance spectroscopy","year":2020,"lang":"en","type":"article","venue":"Endocrinology Diabetes & Metabolism","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of Alberta; University of Lethbridge","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Gestational diabetes; Metabolomics; Medicine; Metabolome; Pregnancy; Diabetes mellitus; Metabolic syndrome; Internal medicine; Bioinformatics; Endocrinology; Biology; Gestation","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":[0.0002918652,0.0002581777,0.0001762515,0.0005646989,0.0001618622,0.0003237774,0.000131381,0.0002481478,0.0004080468],"category_scores_gemma":[0.0006526968,0.0001093463,0.0001514983,0.0005719807,0.0001373195,0.0001238678,0.0002335336,0.0002188718,0.00008304858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002514401,"about_ca_system_score_gemma":0.0002716473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004096391,"about_ca_topic_score_gemma":0.006631654,"domain_scores_codex":[0.9999144,0.00002698488,0.000005243519,0.00001870882,0.00002017223,0.00001448254],"domain_scores_gemma":[0.9998957,0.0000254963,0.00004425827,0.00000692568,0.00001611504,0.00001154605],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001828218,0.0001329,0.6420114,0.0003650351,0.0003060629,0.001153334,0.0003540051,0.0009022517,0.2114619,0.0004607658,0.001087742,0.1399363],"study_design_scores_gemma":[0.00002142896,0.0004481562,0.9545721,0.00008520832,0.0001759362,0.002111764,0.0003571502,0.003674926,0.0351165,0.000623126,0.002792515,0.00002115286],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9873353,0.005978487,0.004000945,0.000424601,0.00002245191,0.0000273298,0.0008031648,0.00005082278,0.001356862],"genre_scores_gemma":[0.9898053,0.003982715,0.005280381,0.0001128559,0.00001588315,0.00002361008,0.0003502731,0.000008029912,0.0004209407],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004096391,"threshold_uncertainty_score":0.008145094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01318078509224522,"score_gpt":0.2343772250862156,"score_spread":0.2211964399939704,"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."}}