Effectiveness of nirmatrelvir-ritonavir for the treatment of patients with mild to moderate COVID-19 and at high risk of hospitalization: Systematic review and meta-analyses of observational studies
Bibliographic record
Abstract
OBJECTIVE: To assess the effectiveness of nirmatrelvir-ritonavir in the treatment of outpatients with mild to moderate COVID-19 who are at higher risk of developing severe illness, through a systematic review with meta-analyses of observational studies. METHODS: A systematic search was performed, in accordance with the Cochrane search methods, to identify observational studies that met the inclusion criteria. The outcomes of mortality and hospitalization were analyzed. Search was conducted on PubMed, EMBASE, and The Cochrane Library. Two reviewers independently screened references, selected the studies, extracted the data, assessed the risk of bias using ROBINS-I tool and evaluated the quality of evidence using the GRADE tool. This study followed the PRISMA reporting guideline. RESULTS: A total of 16 observational studies were finally included. The results of the meta-analysis showed that in comparison to standard treatment without antivirals, nirmatrelvir-ritonavir reduced the risk of death by 59% (OR = 0.41; 95% CI: 0.35-0.52; moderate certainty of evidence). In addition, a 53% reduction in the risk of hospital admission was observed (OR = 0.47; 95% CI: 0.36-0.60, with very low certainty of evidence). For the composite outcome of hospitalization and/or mortality, there was a 56% risk reduction (OR = 0.44; 95% CI: 0.31-0.64, moderate certainty of evidence). CONCLUSION: The results suggest that nirmatrelvir-ritonavir could be effective in reducing mortality and hospitalization. The results were valid in vaccinated or unvaccinated high-risk individuals with COVID-19. Data from ongoing and future trials may further advance our understanding of the effectiveness and safety of nirmatrelvir-ritonavir and help improve treatment guidelines 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 | 0.021 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.039 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".