Prevention and control of acute respiratory viral infections in adult population: A systematic review and meta-analysis on ginseng-based clinical trials
Bibliographic record
Abstract
• Hundreds of millions have been infected, and millions have lost their lives to COVID-19. • Ginseng has been useful in similar respiratory viral infections in the past. • The current paper highlights the need to consider ginseng-based medicines for ARIs including coronavirus infection control. • Ginseng-based medicines were effective in reducing the acute respiratory viral infection risk by over 40%. • When infected, the ginseng-dosed group were less severe, less feverish and recovered 3.4 days faster than the placebo group. Acute respiratory infections are continuously emerging. COV-SARS-2, its variants and most viral respiratory diseases currently do not have a definitive cure. This study aims to evaluate the potential therapeutic effectiveness of ginseng-based medicines for the prevention and control of acute respiratory illness, including SARS-COV-2, among the adult population. We performed a systematic literature review using PubMed, Scopus, Web of Science, and Cochrane trial library for studies published between January 2000 and September 2021. Repeated search was done in December 2023. All related clinical trials that reported the use of ginseng in COVID-19 patients and respiratory viral infections among adults were included in this analysis. Two independent researchers performed the screening. Meta-analysis was performed using Cochrane's review manager (RevMan) version 5.4 . A total of 596 articles were retrieved. After screening, 5 articles with RCT outcomes relevant to the review were selected for the quantitative evaluation. Ginseng was found to be effective in reducing the infection risk by over 40%, and the infected had 37.1% less severe infection, 13% less feverish and a 3.4-day faster recovery from acute respiratory illness (ARI), in ginseng group compared to the placebo. The present study revealed that ginseng-based medicines were effective in reducing the acute respiratory illness (ARI) infection risk by over 40% and when infected, the ginseng dosed group recovered 3.4 days faster than the placebo group. In addition, ginseng lessened the severity and fever from the infection compared to the placebo group.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.036 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".