Herbal Medicine and COVID-19: An Umbrella Review
Bibliographic record
Abstract
Background: Most systems of traditional medicine have been using herbal medicines to prevent and treat acute respiratory conditions and various other conditions for centuries. The aim of this project is to identify and examine the systematic and narrative reviews reporting on the therapeutic use of herbal medicines as it relates to the prevention and treatment of COVID-19 and long COVID. Methods: This paper is part of an umbrella review of studies related to natural health products and natural therapies for the prevention or treatment of COVID-19. It is a follow-up to a live review that was conducted by the World Naturopathic Federation between May 2022 and May 2023. PubMed and Google Scholar were searched for systematic and narrative reviews that met defined quality criteria. Results: Over half of the initial systematic reviews were excluded as they did not meet the inclusion and AMSTAR criteria. The final paper included 25 narrative reviews and 41 systematic reviews (SR), with half of the SRs reporting on the safety of herbal interventions. Various therapeutic properties of over 60 herbal medicines were outlined, some individually and most of them as part of herbal formula (combinations). Conclusion/Summary: Herbal interventions demonstrated statistically significant improved recovery in patients with COVID-19. The most common therapeutic properties identified were immunological properties, anti-inflammatory, anti- microbial, and antioxidant while the most frequently investigated herbs were Glycyrrhiza glabra/uralensis, Tinospora cordifolia, and Curcuma longa. More attention is needed on the regulation of herbal medicines, the quality of research, and the safety of herbal medicines.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".