{"id":"W4387996831","doi":"10.1016/j.xcrm.2023.101254","title":"Sequential multi-omics analysis identifies clinical phenotypes and predictive biomarkers for long COVID","year":2023,"lang":"en","type":"article","venue":"Cell Reports Medicine","topic":"Long-Term Effects of COVID-19","field":"Medicine","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Jewish General Hospital; University of British Columbia; The Metabolomics Innovation Centre; McGill University; University of Calgary; University of Alberta","funders":"Metabolomics Innovation Centre; National Eye Institute; T. Von Zastrow Foundation; Fundació la Marató de TV3; Innovative Medicines Initiative; Canadian Institutes of Health Research; University of Alberta; Northern Alberta Clinical Trials and Research Centre; Canada Research Chairs; European Commission; Österreichischen Akademie der Wissenschaften; European Federation of Pharmaceutical Industries and Associations; Horizon 2020 Framework Programme","keywords":"Convalescence; Metabolome; Metabolomics; Coronavirus disease 2019 (COVID-19); Arginine; Medicine; Proteome; Biomarker; Adverse effect; Biology; Bioinformatics; Immunology; Disease; Internal medicine; Amino acid; Biochemistry; Infectious disease (medical specialty)","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":[0.002697517,0.0003081313,0.001059487,0.0007213795,0.0001502631,0.00002846536,0.0001026381,0.0002444003,0.0000756783],"category_scores_gemma":[0.004619767,0.0002439873,0.0003424468,0.0009434327,0.000742559,0.0001140774,0.0001064807,0.0002314528,0.000009278862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001158395,"about_ca_system_score_gemma":0.0002709642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009650274,"about_ca_topic_score_gemma":0.00004825769,"domain_scores_codex":[0.9969684,0.0001035978,0.00117017,0.0007976969,0.0005253249,0.0004348375],"domain_scores_gemma":[0.9963167,0.001631815,0.000443694,0.0007030401,0.0002483452,0.000656435],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00116661,0.0002540711,0.9262095,0.002576375,0.005412998,0.00695522,0.001294008,0.0003460947,0.006757999,0.00000891205,0.04506142,0.003956805],"study_design_scores_gemma":[0.01240261,0.001976922,0.8957182,0.0005440833,0.02194868,0.0005393508,0.0009497455,0.04999196,0.004802349,0.0009701701,0.009384439,0.0007715392],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9482375,0.001941267,0.03841912,0.004277367,0.002781578,0.00276486,0.00004442734,0.0008089648,0.0007249054],"genre_scores_gemma":[0.992729,0.0008479785,0.001104241,0.0009173496,0.0008344491,0.0001033808,0.001027822,0.00006494253,0.002370855],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04964586,"threshold_uncertainty_score":0.9949515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04241002814668967,"score_gpt":0.3806219381596961,"score_spread":0.3382119100130064,"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."}}