{"id":"W3216598092","doi":"10.21203/rs.3.rs-1062756/v1","title":"Early prediction of COVID-19 patient survival by targeted plasma multi-omics and machine learning","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Jewish General Hospital; McGill University","funders":"Warren Y. Soper Charitable Trust; Fondation De Famille Alvin Segal; Jewish General Hospital; Public Health Agency; National Cancer Institute; Ministry of Science and Higher Education of the Russian Federation; Public Health Agency of Canada; Skolkovo Institute of Science and Technology; Genome Canada; McGill University","keywords":"Coronavirus disease 2019 (COVID-19); Omics; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Computational biology; Computer science; Medicine; Artificial intelligence; Virology; Machine learning; Bioinformatics; Biology; Internal medicine; Outbreak; Disease","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.003178244,0.0003590237,0.0005927297,0.0005660101,0.0005306537,0.0004408539,0.001141428,0.000528174,0.00004904665],"category_scores_gemma":[0.004984894,0.0003745048,0.0001379428,0.000754767,0.0002120757,0.0002135257,0.004697142,0.00459091,0.00000647196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005085976,"about_ca_system_score_gemma":0.001391211,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01146536,"about_ca_topic_score_gemma":0.0002599591,"domain_scores_codex":[0.9912137,0.003936144,0.0007144025,0.001371096,0.001967093,0.0007975852],"domain_scores_gemma":[0.9954245,0.001299436,0.000370312,0.001156895,0.001030593,0.0007182515],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002472563,0.0007524855,0.8326334,0.01344799,0.0002875044,0.0003012962,0.03866389,0.05767423,0.00232991,0.001444606,0.001382232,0.05083522],"study_design_scores_gemma":[0.001076298,0.001024359,0.04243993,0.0005341288,0.000009537143,0.00001905418,0.0008886781,0.9465582,0.0006641407,0.0001746511,0.006185077,0.0004258897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8649545,0.005462038,0.1224909,0.002909115,0.0009361554,0.001833454,0.0008076981,0.0004726446,0.0001335878],"genre_scores_gemma":[0.9847033,0.0009763098,0.01294999,0.00004314517,0.00007934571,0.0001330298,0.0008423023,0.00005652673,0.0002160395],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.888884,"threshold_uncertainty_score":0.9998707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09018038749882826,"score_gpt":0.3808919417793242,"score_spread":0.290711554280496,"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."}}