Efficacy and safety of corticosteroids in COVID-19 based on evidence for COVID-19, other coronavirus infections, influenza, community-acquired pneumonia and acute respiratory distress syndrome: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Very little direct evidence exists on use of corticosteroids in patients with coronavirus disease 2019 (COVID-19). Indirect evidence from related conditions must therefore inform inferences regarding benefits and harms. To support a guideline for managing COVID-19, we conducted systematic reviews examining the impact of corticosteroids in COVID-19 and related severe acute respiratory illnesses. METHODS: We searched standard international and Chinese biomedical literature databases and prepublication sources for randomized controlled trials (RCTs) and observational studies comparing corticosteroids versus no corticosteroids in patients with COVID-19, severe acute respiratory syndrome (SARS) or Middle East respiratory syndrome (MERS). For acute respiratory distress syndrome (ARDS), influenza and community-acquired pneumonia (CAP), we updated the most recent rigorous systematic review. We conducted random-effects meta-analyses to pool relative risks and then used baseline risk in patients with COVID-19 to generate absolute effects. RESULTS: In ARDS, according to 1 small cohort study in patients with COVID-19 and 7 RCTs in non-COVID-19 populations (risk ratio [RR] 0.72, 95% confidence interval [CI] 0.55 to 0.93, mean difference 17.3% fewer; low-quality evidence), corticosteroids may reduce mortality. In patients with severe COVID-19 but without ARDS, direct evidence from 2 observational studies provided very low-quality evidence of an increase in mortality with corticosteroids (hazard ratio [HR] 2.30, 95% CI 1.00 to 5.29, mean difference 11.9% more), as did observational data from influenza studies. Observational data from SARS and MERS studies provided very low-quality evidence of a small or no reduction in mortality. Randomized controlled trials in CAP suggest that corticosteroids may reduce mortality (RR 0.70, 95% CI 0.50 to 0.98, 3.1% lower; very low-quality evidence), and may increase hyperglycemia. INTERPRETATION: Corticosteroids may reduce mortality for patients with COVID-19 and ARDS. For patients with severe COVID-19 but without ARDS, evidence regarding benefit from different bodies of evidence is inconsistent and of very low quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.053 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".