Artesunate, imatinib, and infliximab in COVID‐19: A rapid review and meta‐analysis of current evidence
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Despite the pervasive vaccination program against coronavirus disease 2019 (COVID-19), fully vaccinated people are still being infected by severe acute respiratory syndrome coronavirus 2, making an effective and safe therapeutic intervention a crucial need for the patients' survival. The purpose of the present study is to seek available evidence for the efficacy and safety of three promising medications artesunate, imatinib, and infliximab against COVID-19. METHODS: A literature search was conducted in PubMed, Cochrane Library, medRxive, and Google Scholar up to January 2022. Furthermore, the clinical trial databases were screened to find more citations. The Cochrane Collaboration tool and Newcastle-Ottawa scale were used to assess the included studies. Meta-analysis was performed using RevMan 5.4.1. RESULTS: Five published studies were identified as eligible. Meta-analysis showed that there was no significant difference between the infliximab and control groups in terms of mortality rate (risk ratio [RR]: 0.65; 95% confidence interval [CI]: 0.40-1.07; p = 0.09). However, a significant difference was observed between the two groups for the hospital discharge (RR: 1.37; 95% CI: 1.04-1.80; p = 0.03). No remarkable clinical benefit was observed in favor of using imatinib for COVID-19 patients. Artesunate showed significant improvement in patients with COVID-19. CONCLUSION: In the present, limited evidence exists for the efficacy and safety of artesunate, imatinib, and infliximab in patients with COVID-19. The findings of WHO's Solidarity international trial will provide further information regarding these therapeutic interventions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".