P05.32. A tool for rapid identification of potential herbal medicine-drug interactions 2011 update: a review
Bibliographic record
Abstract
Herbs and drugs reviewed were based on the prevalence of their use. Database searches for herbs were conducted in MEDLINE, EMBASE, and IPA between 2007 and 2010. Herbs were searched with the following terms: ‘clinical trials’, ‘case studies’, and ‘case reports’. Abstracts of each article were read to identify herb-drug interactions. All potential interactions were reviewed by an expert (PG or HB). Reference lists of relevant review articles were analyzed for additional papers, as was the textbook Herb, Nutrient, and Drug Interactions: clinical implications and therapeutic strategies. Data extraction involved classifying the interactions into four groups: (1) No reported or theoretical interactions, (2) Theoretical interactions based on animal or in vitro data, (3) Theoretical interactions extrapolated from clinical data, and (4) Interactions supported by clinical evidence. Two thousand one hundred forty-eight references were identified by the searches, and 117 potential updates are being sent to reviewers. The herbal medicine-drug interaction grid will allow clinicians to have a guide on potential herbal medicine-drug harms based on the most recent literature.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.012 |
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".