Higher- versus Lower-Dose Corticosteroids for Severe to Critical COVID-19: A Systematic Review and Dose-Response Meta-analysis
Bibliographic record
Abstract
Abstract Rationale Corticosteroids are standard of care for patients with severe coronavirus disease (COVID-19). However, the optimal dose is uncertain. Objectives To compare higher doses of corticosteroids with lower doses in patients with COVID-19. Methods We searched MEDLINE, Embase, Cochrane Central Register of Controlled Trials, MedRxiv, and Web of Science from inception to August 1, 2022, for trials that randomized patients with severe-to-critical COVID-19 to corticosteroids, standard care, or placebo. Reviewers, working in duplicate, screened references, extracted data, and assessed risk of bias using a modified version of the Cochrane risk of bias 2.0 tool. We performed a dose–response meta-analysis and used the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework to assess the certainty of evidence. We present our results both in relative risk and absolute risk difference per 1,000, with 95% confidence intervals (CIs). Results We included 20 trials, with 10,155 patients. We show that, compared with lower-dose corticosteroids, higher-dose corticosteroids probably reduce mortality (absolute risk difference, 14 fewer deaths per 1,000 [95% CI, 26 fewer to 2 fewer]; moderate certainty) and may reduce the need for mechanical ventilation (absolute risk difference, 11.6 fewer per 1,000 [95% CI, 23.2 fewer to 6.9 more]; low certainty). The effect of corticosteroids on nosocomial infections is uncertain (16.7 fewer infections per 1,000 [95% CI, 5.4 fewer to 25.0 fewer]; very low certainty). Conclusions Relatively higher doses of corticosteroids may be beneficial in patients with severe-to-critical COVID-19 and may not increase the risk of nosocomial infections.
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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 | 0.024 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.050 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".