Treatment of the common cold with herbs used in Ayurveda and Jamu: monograph review and the science of ginger, liquorice, turmeric and peppermint
Bibliographic record
Abstract
Background: The common cold is typically managed with decongestants, antihistamines, antitussives and antipyretics. In addition to these established medications, herbal ingredients have been used over centuries to help treat common cold symptoms. The Ayurveda and Jamu systems of medicine, originating from India and Indonesia, respectively, have leveraged herbal therapies to treat many illnesses. Method: An expert roundtable discussion comprising specialists in Ayurveda, Jamu, pharmacology and surgery along with a literature review was conducted to evaluate the use of four herbs - ginger, liquorice, turmeric and peppermint - for common cold symptom management in Ayurvedic texts, Jamu publications and monographs from the World Health Organization, Health Canada and various European guidelines. Discussion: Due to a lack of antivirals, common cold management revolves around maintaining personal hygiene and symptom management. Herbal medicines have been an integral part of many cultures worldwide. Despite its growing acceptance, there is a perception that healthcare providers lack interest and may prevent patients from discussing the use of herbal medicines. Limited education and training may also widen the communication gap between patients and healthcare providers, hindering effective management. Conclusion: Evaluation of scientific evidence and the standing in international monographs can offer perspectives on the use of herbal medicines for common cold management.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".