Table2_An updated meta-analysis of Chinese herbal medicine for the prevention of COVID-19 based on Western-Eastern medicine.DOCX
Bibliographic record
Abstract
Background and aims: Chinese herbal medicine (CHM) was used to prevent and treat coronavirus disease 2019 (COVID-19) in clinical practices. Many studies have demonstrated that the combination of CHM and Western medicine can be more effective in treating COVID-19 compared to Western medicine alone. However, evidence-based studies on the prevention in undiagnosed or suspected cases remain scarce. This systematic review and meta-analysis aimed to investigate the effectiveness of CHM in preventing recurrent, new, or suspected COVID-19 diseases. Methods: We conducted a comprehensive search using ten databases including articles published between December 2019 and September 2023. This search aimed to identify studies investigating the use of CHM to prevent COVID-19. Heterogeneity was assessed by a random-effects model. The relative risk (RR) and mean differences were calculated using 95% confidence intervals (CI). The modified Jadad Scale and the Newcastle-Ottawa Scale (NOS) were employed to evaluate the quality of randomized controlled trials and cohort studies, respectively. Results: Seventeen studies with a total of 47,351 patients were included. Results revealed that CHM significantly reduced the incidence of COVID-19 (RR = 0.24, 95% CI = 0.11–0.53, p = 0.0004), influenza (RR = 0.37, 95% CI = 0.18–0.76, p = 0.007), and severe pneumonia exacerbation rate (RR = 0.17, 95% CI = 0.05–0.64, p = 0.009) compared to non-treatment or conventional control group. Evidence evaluation indicated moderate quality evidence for COVID-19 incidence and serum complement components C3 and C4 in randomized controlled trials. For the incidence of influenza and severe pneumonia in RCTs as well as the ratio of CD4<sup>+</sup>/CD8<sup>+</sup> lymphocytes, the evidence quality was low. The remaining outcomes including the disappearance rate of symptoms and adverse reactions were deemed to be of very low quality. Conclusion: CHM presents a promising therapeutic option for the prevention of COVID-19. However, additional high-quality clinical trials are needed to further strengthen evidential integrity.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.187 | 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 teacher head, 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".