Natural health product–drug interaction tool
Bibliographic record
Abstract
Natural health products (NHPs), such as herbal medicines, probiotics, vitamins and minerals, are used regularly by 73% of Canadians.1 Many consumers believe that NHPs derived from plants (such as herbal medicines) are safe because they are “natural.”1 However, some plant-derived NHPs can interact with pharmaceutical medications, potentially resulting in serious harm for patients.2 A recent active surveillance study found that approximately 45% of consumers presenting to a Canadian pharmacy report using NHPs and prescription drugs concomitantly, with 7.4% describing an adverse event.3 It is important for clinicians to initiate patient communication about NHP use and be knowledgeable about the potential risks to ensure patient safety. Unless clinicians ask, they may be unaware of patient NHP use. Most NHPs can be purchased from many sources without involvement of a health professional, and many patients do not disclose NHP use to clinicians.4 However, education about NHP interactions is not always included within medical and pharmacy curricula,5 and new evidence on NHP–drug interactions is constantly emerging. In addition, a quick search of the medical literature might identify contradictory information or poor-quality reports that are challenging to interpret. Clinicians could benefit from effective knowledge translation tools to identify and prevent potential NHP–drug interactions in their patients. As pharmacists have confirmed the utility of a NHP–drug interaction grid6 as an effective knowledge translation tool,7 we undertook a scoping review to identify and incorporate new NHP–drug interactions.
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 | 0.023 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.004 |
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".