Clinical efficacy and adverse events of baricitinib treatment for coronavirus disease‐2019 (COVID‐19): A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract The benefits of baricitinib in coronavirus disease‐2019 are inadequately defined. We performed a systematic review and meta‐analysis of studies of baricitinib to determine its clinical efficacy and adverse events in patients with COVID‐19. Databases were searched from their inception to September 5, 2021. The primary outcome was the coefficient of mortality. We also compared secondary indicators and adverse events between baricitinib treatment and placebo or other treatments. Twelve studies of 3564 patients were included and assessed qualitatively (modified Jadad and Newcastle–Ottawa Scale scores). Baricitinib effectively improved the mortality rate (relative risk of mortality = 0.56; 95% confidence interval: 0.46–0.69; p < 0.001; I 2 = 2%), and this result was unchanged by subgroup analysis. Baricitinib improved intensive care unit admission, the requirement for invasive mechanical ventilation, and improved the oxygenation index. Data from these studies also showed that baricitinib slightly reduced the risk of adverse events. Regarding the choice of the drug dosage of baricitinib, the high‐dose group appeared to have additional benefits for clinical efficacy. Our study shows that baricitinib may be a promising, safe, and effective anti‐severe acute respiratory syndrome‐coronavirus‐2 drug candidate, with the advantages of low cost, easy production, and convenient storage.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.026 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".