{"id":"W2924255801","doi":"10.1016/j.pnpbp.2019.03.010","title":"Advances and challenges in development of precision psychiatry through clinical metabolomics on mood and psychotic disorders","year":2019,"lang":"en","type":"review","venue":"Progress in Neuro-Psychopharmacology and Biological Psychiatry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Western Hospital; University Health Network","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Metabolomics; Schizophrenia (object-oriented programming); Identification (biology); Bipolar disorder; Computational biology; Metabolite; Mood disorders; Medicine; Mood; Psychiatry; Psychology; Bioinformatics; Biology; Internal medicine","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":[0.002755537,0.0009627958,0.002066364,0.001775599,0.0002856996,0.001662535,0.001183391,0.001581645,0.00353734],"category_scores_gemma":[0.002992125,0.0002842981,0.0007210047,0.001680286,0.0009788306,0.001774848,0.001334649,0.002460614,0.001345372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009517962,"about_ca_system_score_gemma":0.002324229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001658257,"about_ca_topic_score_gemma":0.003190502,"domain_scores_codex":[0.9995275,0.0001396426,0.0000474829,0.00009447058,0.0001432736,0.00004763167],"domain_scores_gemma":[0.9975667,0.001649663,0.0001456828,0.00008048795,0.0004482664,0.0001091953],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001344664,0.00006466333,0.0003896616,0.007195965,0.000156149,0.000152388,0.00003801426,0.0003713871,0.001129876,0.008683638,0.0222199,0.959464],"study_design_scores_gemma":[0.0000611963,0.0002246623,0.001925667,0.005581846,0.0002887699,0.0008780697,0.0001176966,0.000484659,0.001009619,0.01645495,0.9729163,0.00005649568],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00008669212,0.9974549,0.000530978,0.001133216,0.0002286092,0.000003605367,0.00002471342,0.00001321726,0.0005240468],"genre_scores_gemma":[0.00116857,0.9956157,0.001143342,0.0009526282,0.0006627054,0.00000980776,0.00004600087,0.000004510741,0.0003966748],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.00353734,"threshold_uncertainty_score":0.01457286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1011400631088412,"score_gpt":0.4136993316796991,"score_spread":0.3125592685708579,"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."}}