Effect of corticosteroid therapy on mortality in COVID‐19 patients—A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract The effect of corticosteroid therapy is still controversial on prevention of mortality in coronavirus disease‐2019 (COVID‐19). The objective of this study is to investigate the effect of corticosteroids on mortality. This systematic review was performed as per preferred reporting items for systematic reviews and meta‐analyses guidelines. A systematic search was performed at different databases namely Medline/PubMed, Cochrane and Google scholar on 10 February 2022. A pooled estimate for effect of corticosteroid therapy on mortality was calculated as outcome of study. Risk bias analysis and Newcastle Ottawa Scale were used to assess the quality of randomized control trial (RCT) and cohort studies, respectively. Cochran's Q test and the I2 statistic were conducted for heterogeneity and accordingly study model was applied. A total 43 studies were included, having sample size of 96,852 patients. Amongst them, 19,426 and 77,426 patients received corticosteroid therapy (intervention group) or standard treatment without corticosteroid (control group), respectively. Mortality observed in the intervention and control group was 14.2% (2749) and 7.1% (5459), respectively. The pooled estimate 2.173 (95% CI: 2.0690–2.2820) showed significantly increased mortality in intervention as compared to control. The pooled estimate of methyprednisolone 1.206 (95% CI: 1.0770–1.3500) showed significantly increased mortality while the pooled estimate of dexamethasone 1.040 (95% CI: 0.9459–1.1440) showed insignificantly increased mortality as compared to control. In conclusion, corticosteroid therapy produced a negative prognosis as depicted by increased mortality among COVID‐19 patients. The possible reasons might be delay in virus clearance and secondary infections due to corticosteroids initiated at high dose in the early stage of infection.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.035 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".