Drug-Herb Interaction Among Commonly Used Conventional Medicines: A Compendium for Health Care Professionals
Bibliographic record
Abstract
The objective of the review was to consolidate the clinical and pharmacologic aspects of drug-herb interactions to develop a compendium of information to provide prescribers with a measure of the risk of interactions, a description of the clinical consequences, and an assessment of the quality (ie, validity) of evidence. A variety of electronic databases and hand-searched references were used to identify documentation of interactions between herbal products and drugs from the most commonly used therapeutic classes. MEDLINE, Allied and Complementary Medicine Database, CINHAL, HealthSTAR, and EMBASE were searched from 1966 to the present. One hundred sixty-two citations were identified. Only 22 citations met the inclusion criteria. Using a matrix of 165 possible drug-herb interaction pairs (15 therapeutic drug classes by 11 herbal products), we identified 51 (31%) interactions discussed in the literature. Twenty-two of these 51 drug-herb pairs (43%) were supported by randomized clinical trials, case-control studies, cohort studies, case series, or case studies. The remaining interaction pairs reflected theoretic reasoning in the absence of clinical data. Most interactions were pharmacokinetic, with most actually or theoretically affecting the metabolism of the affected product by way of the cytochrome p450 enzymes. In this review, warfarin was the most common drug and St. John's wort was the most common herbal product reported in drug-herb interactions. To create a comprehensive and valid list of herb-drug interactions would require a substantial increase in research activities in this area. Improvements in the quality of methodology used are also necessary.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".