{"id":"W4289526446","doi":"10.1016/j.mcpro.2022.100277","title":"Early Prediction of COVID-19 Patient Survival by Targeted Plasma Multi-Omics and Machine Learning","year":2022,"lang":"en","type":"article","venue":"Molecular & Cellular Proteomics","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; University of Victoria; McGill University","funders":"Warren Y. Soper Charitable Trust; Fondation De Famille Alvin Segal; Jewish General Hospital; Ministry of Education and Science of the Russian Federation; Public Health Agency; Génome Québec; Genome British Columbia; National Cancer Institute; Ministère de l'Économie, de l’Innovation et des Exportations du Québec; Public Health Agency of Canada; McGill University; Ministry of Science and Higher Education of the Russian Federation; Fonds de recherche du Québec; Skolkovo Institute of Science and Technology; Genome Canada","keywords":"Coronavirus disease 2019 (COVID-19); Omics; Medicine; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Receiver operating characteristic; 2019-20 coronavirus outbreak; Computational biology; Internal medicine; Bioinformatics; Disease; Intensive care medicine; Biology; Virology; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004250216,0.000252078,0.0003064404,0.0001006623,0.0003409271,0.00002315716,0.0001944973,0.0001146465,0.00002561893],"category_scores_gemma":[0.0002148927,0.0002813828,0.0001240373,0.0001735452,0.0001077333,0.000004445114,0.0005783936,0.0003124567,7.528656e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005806464,"about_ca_system_score_gemma":0.00009249455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001542342,"about_ca_topic_score_gemma":0.000005993437,"domain_scores_codex":[0.998192,0.0003070446,0.0003785991,0.0005286592,0.000284932,0.0003087365],"domain_scores_gemma":[0.9991882,0.00001247769,0.0002649913,0.0003072174,0.0000644468,0.0001626913],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001792584,0.0001414703,0.004244394,0.00003933953,0.0001671947,0.0000127039,0.0001864491,0.00162573,0.9929131,0.0001389671,0.0001242437,0.0002271059],"study_design_scores_gemma":[0.001484343,0.0009575037,0.0001579751,0.000001764199,0.00005219695,0.00001428782,0.0002176003,0.003700352,0.9592825,0.00005297602,0.03382181,0.000256701],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9570087,0.004379714,0.03701165,0.0001204619,0.0001803192,0.0006441321,0.0005787612,0.00002387709,0.00005244796],"genre_scores_gemma":[0.9930258,0.0004848311,0.004881168,0.000138662,0.0000264924,0.0001320845,0.001033114,0.0000554626,0.0002224011],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03601714,"threshold_uncertainty_score":0.9999638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01142509626776603,"score_gpt":0.2092569433646982,"score_spread":0.1978318470969322,"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."}}