Which medications used in paediatric practice have demonstrated natural health product-drug interactions?
Bibliographic record
Abstract
A recent Canadian survey (1) of 1804 children and youth suggested that 49% had tried one or more complementary and alternative medicine therapies and 41% had used a natural health product (NHP) in the previous year. Similarly, a three-month survey (2) of 142 families in an American paediatric emergency department suggested that 45% of caregivers had given their children an herbal remedy in the previous year. NHP use may be much higher within certain subgroups of the child and youth population (eg, children with serious and/or chronic conditions, such as cancer, cystic fibrosis, asthma, attention deficit hyperactivity disorder and eating disorders) (3–7). In particular, NHP use may be highest in paediatric populations with chronic disease who suffer relapses or other setbacks (3). Surveys have also shown that rates of NHP use are high (70%) among homeless youth, many of whom suffer from chronic mental and physical health conditions (8). Indeed, because children with serious, chronic or recurrent illnesses are the most likely to be on concurrent prescription medications, they may have the highest risk for clinically relevant 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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".