Hepatotoxic Botanicals - An Evidence-based Systematic Review
Bibliographic record
Abstract
PURPOSE: Herbal medicines have been increasingly used worldwide. However, the potential harms of these herbs have been noticed most recently following hepatotoxicity with ingestion of herbal remedies. The aim of this review is to evaluate the evidence of hepatotoxic effects linked to use of herbal preparations. METHOD: Electronic search was performed by searching several databases: PubMed, HerbMed, Google Scholar, Scopus, Cochrane Database of Systematic Reviews and Cochrane Library using both Latin and common names of several herbs. Language was restricted to English and articles were selected for relevance reporting incidence of hepatotoxicity associated with use of herbal products in human. RESULTS: From a total of 565 relevant reviews and articles, 254 met our inclusion criteria and were analyzed. Serious hepatotoxic events associated with various herbal products alone or in combination with other drugs have been reported. Linking to herbal constituents the spectrum of liver toxicity includes elevated liver enzymes, acute or chronic hepatitis, cholestasis, hepatic necrosis, fibrosis, and cirrhosis, as well as acute liver failure and hepatic veno-occlusive disease. CONCLUSION: The hepatotoxicity of herbs was extensively acknowledged. As the use of natural medicine increases, the risk of liver toxicity and drug interaction increase as well. Accordingly, herbal remedies have been known as hepatotoxins causing several liver damages. Further scientific studies with high and good quality are needed to identify toxic compounds and understand the exact mechanism of hepatotoxicity-induced by herbs. The adverse effects of herbal products must be fully reported as well as extensive education of healthcare providers must be provided in order to reduce danger of alternative medicines.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".