Natural Health Product-Drug Interactions
Bibliographic record
Abstract
Interactions between natural health products (NHP) and prescription medications are of increasing concern. This paper aims to identify all clinical trials of NHP-drug interactions. To determine the prevalence and outcomes of clinical investigations of NHP-drug pharmacokinetic interactions, electronic databases were searched from inception through March 2004, as well as reference lists from published reports and experts in the field for unpublished studies. Eligible studies were clinical investigations of the interaction between a NHP and the metabolism of a regulated medication in humans. Studies were excluded that only investigated the metabolism of an NHP or examined food-drug or NHP-NHP interactions. Two reviewers selected studies for inclusion and independently extracted data. Forty-seven trials were identified, studying an average of 14 participants/study (95% confidence interval [CI] 11-18), examined drug interactions with 19 different herbal preparations. All trials were pharmacokinetic studies, 41 of healthy volunteers and 6 of patients. Ten different herbal medicines as well as 5 different traditional herbal concoctions were studied. Potentially clinically significant drug interactions were observed with St. John wort (16/24 studies), garlic (2/5 studies), and American ginseng (1 study). Research on NHP-drug interactions is limited in number and scope. With the exception of St. John wort, clinicians and the public do not have information that permits strong inferences about interactions between NHPs and conventional medications.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".