Comparison of the efficacy and safety of antiviral agents for COVID-19: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective: To compare the efficacy and safety of antiviral agents currently studied for the treatment of the COVID-19 pandemic. Methods: A literature search was conducted on the PubMed, EMBASE, Web of Science, CNKI (Chinese Database), and MedRxiv for studies published from 1966 till May 10, 2020, and identified articles containing “COVID-19” and “antiviral agents”. Studies were reviewed and screened in the guidance of PRISMA. STATA 15.1 software was used to build a random-effects model. Heterogeneity was assessed using I2. The Cochrane Risk of Bias or Newcastle-Ottawa-Scale (NOS) was employed to evaluate the public bias. Results: We identified 916 papers and included 7 studies involving 878 patients. The network meta-analysis was centered on comparing the efficacy and safety of presently used antiviral drugs for COVID-19. Among the antiviral agents applied in the treatment of COVID-19 treatment, including lopinavir/ritonavir, remdesivir, favipiravir, arbidol (umefenovir) or placebo, favipiravir exhibited significantly better efficacy in nucleic acid conversion rate [RR 2.38, 95%CI (1.05, 5.41)] and CT improvements [RR 1.85, 95%CI (1.07, 3.2)] than arbidol as well as other included antivirals though no significant association were found. ARB had advantages in nucleic acid conversion rate and ADRs incidence. Besides, favipiravir was more superior in safety than other antiviral drugs assessed by SUCRA (Surface Under the Cumulative Ranking Curve) ranking. Conclusions: Favipiravir had better efficacy in clinical recovery as well as more acceptable safety, though further clinical research should be designed to confirm these results. With the limited data of remdesivir, we could not conclude a statistical advantages of using remdesivir.
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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 | 0.008 | 0.221 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.021 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".