Small-molecule antiviral treatments for COVID-19: A systematic review and network meta-analysis
Bibliographic record
Abstract
OBJECTIVE: This study aimed to explore the efficacy and safety of small-molecule antivirals for treating coronavirus disease 2019 (COVID-19). METHODS: Seven databases were searched from their inception to 01 June 2023. The risk of bias in randomised controlled trials and retrospective studies was evaluated individually using the Cochrane risk-of-bias tool and Newcastle Ottawa Scale. RESULTS: In total, 160 studies involving 933 409 COVID-19 patients were evaluated. Compared with placebo or standard of care, proxalutamide demonstrated remarkable efficacy in reducing mortality rates, hospitalisation rates, serious adverse events, and the need for mechanical ventilation. Furthermore, it significantly enhanced both the rate of clinical improvement and expedited the duration of clinical recovery when compared with control groups. In patients with mild-to-moderate COVID-19, proxalutamide exhibited the above advantages, except for mortality reduction. Triazavirin was the most effective treatment for reducing the time required for viral clearance and improving the discharge rate. Leritrelvir and VV116 were ranked first in terms of enhancing the viral clearance rate on days 7 and 14, respectively. Molnupiravir was the most effective treatment for reducing the need for oxygen support. Overall, these findings remained consistent across the various subgroups. CONCLUSIONS: A thorough evaluation of effectiveness, applicable to both mild-to-moderate and unstratified populations, highlights the specific advantages of proxalutamide, nirmatrelvir/ritonavir, triazavirin, azvudine, molnupiravir, and VV116 in combating COVID-19. Additional clinical data are required to confirm the efficacy and safety of simnotrelvir/ritonavir and leritrelvir. The safety profiles of these antivirals were deemed acceptable.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.009 | 0.009 |
| 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) | 0.004 | 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".