Efficacy of Lopinavir–Ritonavir Combination Therapy for the Treatment of Hospitalized COVID-19 Patients: A Meta-Analysis
Bibliographic record
Abstract
Aim: To evaluate the efficacy and safety of lopinavir–ritonavir (LPV/r) therapy in treating hospitalized COVID-19 patients. Materials & methods: Data from randomized and observational studies were included in meta-analyses. Primary outcomes were length of stay, time for SARS-CoV-2 test conversion, mortality, incidence of mechanical ventilation, time to body temperature normalization and incidence of adverse events. Results: Twenty-four studies (n = 10,718) were included. LPV/r demonstrated no significant benefit over the control groups in all efficacy outcomes. The use of LPV/r was associated with a significant increase in the odds of adverse events. Conclusion: Given the lack of efficacy and increased incidence of adverse events, the clinical use of LPV/r in hospitalized COVID-19 patients is not recommended.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.019 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".