Efficacy and safety of arbidol (umifenovir) in patients with COVID‐19: A systematic review and meta‐analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 it