{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006656265,0.0005572872,0.0007274896,0.00112211,0.0003751806,0.0009590124,0.0002430541,0.0003947814,0.0008590873],"category_scores_gemma":[0.001234381,0.000141379,0.0006004966,0.001134953,0.0001908399,0.0003801033,0.0006688856,0.0005422803,0.000216781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004094203,"about_ca_system_score_gemma":0.0004192258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001209378,"about_ca_topic_score_gemma":0.001582169,"domain_scores_codex":[0.9996263,0.0001008246,0.00003948671,0.0001064693,0.00005120502,0.00007576551],"domain_scores_gemma":[0.9993687,0.000155322,0.0002266617,0.0000661197,0.00009039174,0.0000927653],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001740389,0.0002129673,0.9257334,0.0001153555,0.0004287967,0.0004098661,0.000130446,0.00225599,0.04013461,0.0002282462,0.001128816,0.02748113],"study_design_scores_gemma":[0.00002818271,0.0004260748,0.9723951,0.00002753235,0.0001996049,0.0005585653,0.0003160512,0.01755191,0.006133677,0.0009291776,0.00141259,0.00002165252],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925132,0.0008194017,0.003679497,0.0002560697,0.00002656714,0.00004066961,0.002095383,0.00005093558,0.0005182568],"genre_scores_gemma":[0.9935415,0.0002877351,0.002709677,0.00009882342,0.00002896017,0.00004714816,0.003111997,0.000008443948,0.0001657075],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001209378,"threshold_uncertainty_score":0.003520191,"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."}}