{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0002812477,0.0005728962,0.001434337,0.001079232,0.0001016703,0.00005616454,0.000336964,0.0007017181,0.0004801991],"category_scores_gemma":[0.005846811,0.0004819656,0.0001900609,0.0006435109,0.00008485169,0.0001115886,0.0002230687,0.002720022,0.0001250996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002330624,"about_ca_system_score_gemma":0.001980476,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02976221,"about_ca_topic_score_gemma":0.0007275454,"domain_scores_codex":[0.9959311,0.0001386829,0.001055292,0.00100375,0.001122777,0.000748323],"domain_scores_gemma":[0.9967891,0.0008071961,0.0003464921,0.0006589571,0.0001680095,0.001230252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003886513,0.00004144882,0.2445813,0.004528495,0.0001667491,0.007509139,0.0122772,0.00000386937,0.001126489,6.295732e-7,0.7130927,0.01628332],"study_design_scores_gemma":[0.006774488,0.001829455,0.04880813,0.004171565,0.0004712144,0.00007174654,0.002236484,0.0001627175,0.0003310757,0.00006179821,0.9343966,0.0006847411],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.03692607,0.00078583,0.001139865,0.9569325,0.001780713,0.001545809,0.0003849016,0.0002979737,0.0002063129],"genre_scores_gemma":[0.1328508,0.00004359684,0.0004603453,0.8519235,0.01088314,0.00006237075,0.003255049,0.0001069372,0.0004142449],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.2213039,"threshold_uncertainty_score":0.9997632,"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."}}