Herbal treatment in asthma and COPD – current evidence
Bibliographic record
Abstract
Asthma and COPD are chronic inflammatory respiratory diseases affecting millions of people worldwide and increasing in prevalence. Current treatment options for both conditions aim to alleviate symptoms, improve patient quality of life and prevent disease progression rather than cure the underlying condition itself. Treatments available to patients include anti-inflammatory therapies, bronchodilators and inhaled corticosteroids. While these treatments have been deemed effective at alleviating disease symptoms and exacerbations they are not without their limitations. Overuse of some therapies can induce adverse effects in some patients such as increased risk of developing pneumonia and cardiac co-morbidities. Along with the risks of side effects not all patients will respond to the current treatment options and therefore there is an unmet need for additional or alternative treatments for asthma and COPD. One such area showing good promise in providing an alternative or add-on therapy is that of phytomedicine. Phytomedicine is defined as a plant-based traditional medical practice that uses various plant materials in modalities considered both preventive and therapeutic. This review focuses on recent human clinical trials using plant-based medicines for the treatment of asthma and COPD.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".