Bibliographic record
Abstract
PURPOSE: Herbal medicinal products (HMPs) are again highly popular. Their current popularity renders the assessment of their safety an urgent necessity. METHOD: Narrative review using examples only. RESULTS: Constituents of HMPs can be toxic and numerous examples of liver, kidney or other organ damage are on record. All HMPs contain a range of pharmacologically active constituents, and users of HMPs often combine HMPs with prescribed drugs. Thus herb-drug interactions are a real possibility. In most countries, HMPs are not submitted to stringent regulation and control. Thus unreliable quality can be a problem. In particular, this poses a risk when HMPs are contaminated (e.g. with heavy metals) or adulterated (e.g. with prescription drugs). The medical literature holds numerous examples for all of these scenarios and some are used in this article to illustrate the above points. As this area is grossly under-researched, it is rarely possible to define the size of the problem. CONCLUSIONS: It is concluded that the widespread notion of HMPs being inherently safe is naive at best and dangerous at worst. More research is required to minimise the risk HMPs may pose to consumers' health.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".