A Strategy for Controlling Potential Interactions Between Natural Health Products and Chemotherapy
Bibliographic record
Abstract
The high prevalence of complementary and alternative medicine use including natural health products (NHPs) in the pediatric oncology population is well established. The potential for concurrent use of NHPs with conventional chemotherapy necessitates physician awareness regarding the potential risks and benefits that might come from this coadministration. Knowledge of interactions between NHPs and chemotherapy is poorly characterized; however, an understanding of potential mechanisms of interaction by researchers and clinicians is important. Concerns regarding the use of antioxidants during chemotherapy are controversial and evidence exists to support both adherents and detractors in this debate. Our review addresses issues regarding potential interactions between NHPs and chemotherapies used in pediatric oncology from a pharmacokinetic and pharmacodynamic perspective. Examples of combinations of NHP and chemotherapies are briefly presented in addition to a strategy to avoid (or induce) a possible interaction between a NHP and chemotherapy. In conclusion, more clinical research is needed to substantiate or preclude the use of NHPs in the treatment of cancer and especially in combination with chemotherapy.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".