Yi-Qing Oral Solution and Paxlovid combination for COVID-19: Efficacy and safety in a randomized controlled trial
Bibliographic record
Abstract
Background: The COVID-19 pandemic has exerted a significant social burden worldwide. Traditional Chinese medicine has emerged as a vital role in the clinical management of COVID-19. This study aims to evaluate the efficacy and safety of Yi-Qing Oral Solution in the treatment of SARS-CoV-2 infection. Methods: We conducted a randomized, double-blind, placebo-controlled clinical trial to assess the efficacy and safety of Yi-Qing Oral Solution for COVID-19. A total of 113 patients with mild to moderate COVID-19 were enrolled. The intervention group, consisting of 56 patients, received Yi-Qing Oral Solution in conjunction with Paxlovid, while the control group, comprising 57 patients, received only Paxlovid. We assessed the time to negative nucleic acid test results, clinical symptom scores, and changes in inflammatory markers, liver and kidney function, and electrolyte levels. Results: Our findings indicate that the combination of Yi-Qing Oral Solution and Paxlovid significantly reduces the time required for nucleic acid tests to turn negative and demonstrates greater efficacy than Paxlovid alone in alleviating COVID-19 symptoms, particularly cough ([Formula: see text]). No severe adverse effects, liver or kidney damage, or electrolyte imbalances were reported. Conclusion: The results suggest that Yi-Qing Oral Solution, combined with Paxlovid, may inhibit viral replication, accelerate viral clearance, and facilitate recovery in patients with asymptomatic to moderate COVID-19. This combination could represent a promising therapeutic strategy, particularly in conjunction with other antiviral agents, such as Paxlovid.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".