Efficacy of Lopinavir/Ritonavir Compared With Standard Care for Treatment of Coronavirus Disease 2019 (COVID-19): A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Coronavirus disease 2019 (COVID-19) is a newly discovered multi-organ disease caused by the novel coronavirus SARS-CoV-2. Currently, there are no official guidelines on the pharmacological treatment of COVID-19. Lopinavir/ritonavir is a licensed antiviral treatment against HIV and has shown activity against other coronaviruses. OBJECTIVE: In this study, we review the evidence of the use of lopinavir/ritonavir as a potential treatment candidate against COVID-19. METHODS: This systematic review has been registered in PROSPERO (CRD42020182067). A systematic search of the literature for the observational and randomized controlled trial was conducted in PubMed, PubMed Central, and Google Scholar through May 2nd, 2020. Two reviewers were independently searched and selected. The risk of bias was evaluated using the Jadad scale, Newcastle- Ottawa Quality assessment tool, and the National Institute of Health quality assessment tool. RESULTS: A total of 1,965 articles were screened, from which 6 articles were selected. Of 6 articles that were included in this study, 4 reported no significant benefit in clinical improvement with lopinavir/ ritonavir when compared to standard care of treatment, while 2 studies reported otherwise. Lopinavir/ritonavir was also not associated with a reduction of 28-day mortality rate as reported by 1 included study. Most included studies reported gastrointestinal symptoms as side effects from lopinavir/ritonavir therapy. CONCLUSION: There is not yet enough evidence to support the regular use of lopinavir/ritonavir in the treatment of COVID-19. Further clinical trials are needed to evaluate lopinavir/ritonavir's efficacy in treatment.
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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.007 | 0.007 |
| 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) | 0.005 | 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".