Efficacy and safety of arbidol (umifenovir) in patients with COVID‐19: A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: To provide the latest evidence for the efficacy and safety of arbidol (umifenovir) in COVID-19 treatment. METHODS: A literature systematic search was carried out in PubMed, Cochrane Library, Embase, and medRxiv up to May 2021. The Cochrane risk of bias tool and Newcastle-Ottawa scale were used to assess the quality of included studies. Meta-analysis was performed using RevMan 5.3. RESULTS: Sixteen studies were met the inclusion criteria. No significant difference was observed between arbidol and non-antiviral treatment groups neither for primary outcomes, including the negative rate of PCR (NR-PCR) on Day 7 (risk ratio [RR]: 0.94; 95% confidence interval (CI): 0.78-1.14) and Day 14 (RR: 1.10; 95% CI: 0.96-1.25), and PCR negative conversion time (PCR-NCT; mean difference [MD]: 0.74; 95% CI: -0.87 to 2.34), nor secondary outcomes (p > .05). However, arbidol was associated with higher adverse events (RR: 2.24; 95% CI: 1.06-4.73). Compared with lopinavir/ritonavir, arbidol showed better efficacy for primary outcomes (p < .05). Adding arbidol to lopinavir/ritonavir also led to better efficacy in terms of NR-PCR on Day 7 and PCR-NCT (p < .05). There was no significant difference between arbidol and chloroquine in primary outcomes (p > .05). No remarkable therapeutic effect was observed between arbidol and other agents (p > .05). CONCLUSION: The present meta-analysis showed no significant benefit of using arbidol compared with non-antiviral treatment or other therapeutic agents against COVID-19 disease. High-quality studies are needed to establish the efficacy and safety of arbidol for COVID-19.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.035 |
| Bibliometrics | 0.007 | 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".