Clinically based evidence of drug–herb interactions: a systematic review
Bibliographic record
Abstract
IMPORTANCE OF THE FIELD: Healthcare practitioners are deeply concerned about drug-herb interactions and how concurrent administration may affect both the safety and effectiveness of prescribed drugs. Interactions between botanical medicines and synthetic drugs can be clinically relevant and it is important to understand what kinds of interactions are possible. Better knowledge in this area will help avoid negative interactions and may also help enable synergistic interactions. AREAS COVERED IN THIS REVIEW: Includes articles related to the investigation of Western botanicals or whole herbal extracts in human subjects, investigating either the impact on Cytochrome P450 isoenzymes or an assessment of specific drug-herb interactions within a clinical trial. Searches were conducted in both Pubmed and EMBASE from inception to March 2009. WHAT THE READER WILL GAIN: Knowledge regarding specific interactions to consider in clinical practice. The reader will also gain an appreciation of the complexities associated with the area of drug-herb interactions. Summary tables of relevant drug-herb interactions are presented both for the individual herbs included and at the level of the CYP450 enzymes. TAKE HOME MESSAGE: Knowledge of drug-herb interactions is limited and much more research needs to be done to further document clinically relevant interactions. Even though preclinical data are often poorly generalizable to the human situation, caution must be taken in the absence of clinical evidence especially where drugs with narrow therapeutic windows are concerned.
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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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".