Herbal Medicine and COVID-19: An Umbrella Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it