A Review of the Toxicity of Compounds Found in Herbal Dietary Supplements
Bibliographic record
Abstract
Use of herbal dietary supplements by the public is common and has been happening for centuries. In the United States, the Food and Drug Administration has a limited scope of regulation over marketed herbal dietary supplements, which may contain toxic botanical compounds that pose a public health risk. While the Food and Drug Administration has made efforts to prohibit the sale of unsafe herbal dietary supplements, numerous reports have proliferated of adverse events due to these supplements. This literature review investigates bioactive plant compounds commonly used in herbal dietary supplements and their relative toxicities. Using primarily the National Library of Medicine journal database and SciFinder for current reports, 47 toxic compounds in 55 species from 46 plant families were found to demonstrate harmful effects due to hepatic, cardiovascular, central nervous system, and digestive system toxicity. This review further contributes a novel and comprehensive view of toxicity across the botanical dietary market, and investigates the toxicity of the top ten botanical dietary supplements purchased in the United States of America to gauge the exposure risk of toxicity to the public. The criteria of measuring toxicity in this review (plant compound, family, quantity, and toxicity effects) across the entire market in the United States, with special attention to those supplements whose exposure to the consumer is maximal, provides a unique contribution to the investigation of botanical supplements.
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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 | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".