Lopinavir/Ritonavir in the Treatment of COVID-19: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objective: To systematically evaluate the efficacy and safety of lopinavir/ritonavir (LPV/r) in the treatment of COVID-19. Methods: PubMed, Embase, Ovid, CNKI, CBM, Wanfang, and VIP databases were searched to obtain the clinical studies of LPV/r in the treatment of COVID-19 from December 2019 to July 2020. The literatures were screened according to the inclusion and exclusion criteria. Their qualities were evaluated according to the Newcastle-Ottawa Scale (NOS) and RevMan 5.3 software was used for meta-analysis. Results: A total of 688 patients were included in five studies, involving China and France. Compared with patients in the control group, who was only treated with routine treatment, there were no significant differences of the 7-day nucleic acid negative conversion rate and 14-day nucleic acid negative conversion rate in the treatment group. However, the use of LPV/r increased the incidence of adverse reactions in the treatment group compared to the control group. Conclusion: There is no available evidence to support the use of Lopinavir/ritonavir in the treatment of COVID-19.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.022 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".