{"id":"W3096388455","doi":"10.1016/j.ebiom.2020.103090","title":"Predicting outcomes in COVID-19: From internal validation to improving care","year":2020,"lang":"en","type":"letter","venue":"EBioMedicine","topic":"COVID-19 and healthcare impacts","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Betacoronavirus; Coronavirus Infections; MEDLINE; Medicine; Virology; Computer science; Biology; Internal medicine; Outbreak; Disease; Infectious disease (medical specialty)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2514428,0.003005391,0.003703862,0.005682118,0.001509515,0.009190914,0.005411038,0.002772552,0.003713927],"category_scores_gemma":[0.415394,0.001412557,0.003752459,0.006812595,0.00431291,0.00588203,0.01074347,0.007955587,0.002565353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00344763,"about_ca_system_score_gemma":0.009626426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006644241,"about_ca_topic_score_gemma":0.006920961,"domain_scores_codex":[0.7841821,0.1537334,0.01575249,0.01295999,0.03050621,0.002865691],"domain_scores_gemma":[0.5896932,0.2776078,0.02995243,0.04909369,0.04716275,0.006490068],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003033054,0.0009203246,0.7172698,0.003366755,0.006681995,0.0001622972,0.001945056,0.006898389,0.0005449983,0.007271754,0.07114983,0.1807558],"study_design_scores_gemma":[0.002001119,0.004369386,0.6136279,0.02501397,0.005901778,0.001367879,0.004116294,0.1530655,0.004581395,0.06312308,0.1221079,0.0007237411],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"commentary","genre_scores_codex":[0.4171454,0.05918129,0.3050267,0.08486995,0.008518189,0.01433917,0.06021431,0.005773887,0.04493107],"genre_scores_gemma":[0.7972946,0.008197179,0.1352937,0.01153297,0.002623795,0.006827309,0.03568988,0.0008874868,0.001653058],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.2514428,"threshold_uncertainty_score":0.9231043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05912411922714857,"score_gpt":0.3935926807890017,"score_spread":0.3344685615618532,"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."}}