Lopinavir/Ritonavir for COVID-19: a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: To provide the latest evidence on the efficacy and safety of lopinavir/ritonavir compared to other treatment options for COVID-19. METHODS: We searched PubMed, Cochran Library, Embase, Scopus, and Web of Science for the relevant records up to April 2021. Moreover, we scanned MedRxiv, Google Scholar, and clinical registry databases to identify additional records. We have used the Newcastle-Ottawa Scale and Cochrane risk of bias tools to assess the quality of studies. This Meta-analysis was conducted using RevMan software (version 5.3). RESULTS: Fourteen studies were included. No significant difference was observed between lopinavir/ritonavir and non-antiviral treatment groups in terms of negative rate of PCR (polymerase chain reaction) on day 7 (risk ratio [RR]: 0.83; 95% CI: 0.63 to 1.09; P=0.17), and day 14 (RR: 0.93; 95% CI: 0.81 to 1.05; P=0.25), PCR negative conversion time (mean difference [MD]: 1.09; 95% CI: -0.10 to 2.29; P=0.07), secondary outcomes, and adverse events (P>0.05). There was no significant difference between lopinavir/ritonavir and chloroquine as well as lopinavir/ritonavir and hydroxychloroquine regarding the efficacy outcomes (P>0.05). However, lopinavir/ritonavir showed better efficacy than arbidol for the same outcomes (P<0.05). Lopinavir/ritonavir plus arbidol was effective compared to arbidol alone in terms of the negative rate of PCR on day 7 (P=0.02). However, this difference was not significant regarding other efficacy outcomes (P>0.05). CONCLUSION: Lopinavir/ritonavir has no more treatment effects than other therapeutic agents used herein in COVID-19 patients.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.039 |
| Bibliometrics | 0.008 | 0.008 |
| 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".