Assessing the Efficacy and Challenges of Tofacitinib in the Management of COVID-19: A Systematic Review and Meta-analysis of Cohort Studies
Bibliographic record
Abstract
Objectives: Global healthcare faces challenges in combating COVID-19, with rising cases despite widespread vaccination. Severe COVID-19 cases, marked by acute respiratory distress and cytokine release syndrome, highlight the importance of managing cytokine storms. Janus kinase (JAK) inhibitors, such as tofacitinib, show promise in this regard. While tofacitinib is recommended for severe cases, challenges include adverse effects, conflicting studies, and the need for further investigation of new virus strains. Overcoming these hurdles is crucial for developing an effective treatment protocol and reducing COVID-19 mortality. Methods: This study conducted a comprehensive search across PubMed, Scopus, and ISI Web of Science for observational studies on tofacitinib treatment in human adults with COVID-19. The search covered a specified period up to 2024. Data extraction, including study characteristics and quality assessment, employed the Newcastle Ottawa Scale and a modified Cochrane tool. Statistical analysis, conducted with Comprehensive Meta-Analysis Software, assessed heterogeneity and significance levels. Results: The meta-analysis of the three studies showed a significant reduction in mortality (Risk Ratio: 0.372, 95% CI: 0.213–0.649, P-value = 0.001) with low heterogeneity (Cochrane P-value = 0.793), while no significant reduction in the need for mechanical ventilation was observed (Cochrane P-value = 0.194). Conclusion: Tofacitinib administration shows a significant reduction in COVID-19 mortality. However, the limited studies on its efficacy highlight the need for cautious interpretation in clinical assessments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".