P02.148. Assessing interactions between herbal medicines and drugs: updated review
Bibliographic record
Abstract
Three databases (Medline, Embase, and International Pharmaceutical Academy) were searched for studies conducted from 2007-2010 pertaining to NHP-drug interactions. In addition, National Medicines Comprehensive Database (NMCD) was searched in July 2011. As a cross check tool to verify that no interaction was overlooked, “Herb, nutrient, and drug interactions: clinical implications and therapeutic strategies” (Stargrove et al, 2008) was reviewed to find literature pertaining to NHP-drug interactions. Potentially relevant studies were identified from the primary and secondary literature, and if an interaction was found, the interaction was verified by a second reviewer. To date, 1997 studies have been identified from the database search (1910), textbook and NMCD (87) and screened for interactions. Examination of these studies for interactions is ongoing. This update is intended to increase the knowledge about NHP-drug interactions as well as to fill in any gaps that may have been overlooked in construction of the original tool. The NHP-drug interaction tool is intended to act as a quick guide for users of NHPs and pharmaceuticals in order to avoid adverse reactions. Future steps in this project will include further updates to the tool as well as creating specific grids for different clinical specialties.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".