Arbidol against COVID-19: A Comprehensive Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objective: To provide the latest evidence on the efficacy and safety of Arbidol (Umifenovir) in COVID-19. Methods: A comprehensive systematic search of the evidence was carried out in PubMed, Cochran library, Embase, and Medrxiv up to October 1, 2020. The Cochrane risk of bias tool and Newcastle-Ottawa Scale checklist were used for assessing the quality of studies. Meta-analysis was performed using RevMan (version 5.3). Results: Fifteen studies were met for the inclusion. No significant difference was observed between Arbidol and control groups in terms of primary outcomes, including negative rate of PCR (NR-PCR) on 7 days (risk ratio [RR] 0.89; P=0.37) and 14 days (RR: 1.10;P=0.17), negative conversion time (NCT) (mean difference [MD]: 0.74; P=0.37), and as well as secondary outcomes (P<0.05). Compared with LPV/r, Arbidol showed a better efficacy in terms of NR-PCR on 14 days (P=0.02). In contrast, NCT in LPV/r was higher (P=0.007). However, not significant difference was found in terms of NR-PCR on 7 days (P=0.05). Adding Arbidol to LPV/r was led to a better efficacy in terms of NR-PCR on 7 days and NCT (P<0.05). Nevertheless, it was not significant reading NR-PCR on 14 days (P=0.99). There is no significant difference Arbidol vs. Interferon /Arbidol and IFN/Arbidol vs. Interferon (P<0.05). Conclusion: Arbidol was not superior to control against COVID-19. Additionally, not major treatment effect was found compared with other therapeutic agents. There are needed well-designed studies with large sample size to establish on efficacy and safety of Arbidol.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.029 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".