{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008262569,0.0007078224,0.002048306,0.0002582948,0.00008798681,0.00001517745,0.0003645472,0.0009900515,0.000006645298],"category_scores_gemma":[0.00008783109,0.0005028602,0.0002252325,0.0002532445,0.0006715915,0.00001319858,0.0003584134,0.0008343445,0.000001937218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008120728,"about_ca_system_score_gemma":0.0001271024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.723099e-7,"about_ca_topic_score_gemma":0.0000476327,"domain_scores_codex":[0.9956282,0.0006436934,0.001430451,0.001661503,0.000130698,0.0005055016],"domain_scores_gemma":[0.998589,0.0002965012,0.0006189798,0.0003565281,0.00002638933,0.0001126435],"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":[0.0009354026,0.00102629,0.1422511,0.004320667,0.0002199378,0.000001334977,0.00002879705,3.076669e-7,0.000003131792,0.0009328449,0.00006232677,0.8502179],"study_design_scores_gemma":[0.004607063,0.002441595,0.1102939,0.001368712,0.0002773414,0.00003153972,0.00008948347,0.000003012186,0.000001731059,0.003026161,0.8769962,0.0008632671],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.07851127,0.9167048,0.000004969352,0.0005704639,0.002665185,0.001197249,0.00002597237,0.00001160116,0.0003084892],"genre_scores_gemma":[0.008239015,0.9871197,0.003866476,0.0002567509,0.0002328939,0.0002069208,0.00003327456,0.00003961839,0.000005373022],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8769338,"threshold_uncertainty_score":0.9997423,"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."}}