Green Tea Consumption Is Increasing but There Are Significant Hepatic Side Effects
Bibliographic record
Abstract
Herbal medicines including teas and plant extracts have been in use for thousands of years. There are reports of the use of herbal preparations in Egypt, China, India, and Samaria. Many patients consider “natural” herbal teas to be completely free of unwanted side effects. Many herbal products, however, have biological activities that can result in severe hepatic cell toxicity or interact with other herbal products or prescription medications. Their use is increased dramatically. The most common herbal teas and nonmineral natural products are used as self-medication, principally for health improvement. However, these products are potentially dangerous to some individuals. Monitoring for liver injury is an important aspect of drug and herbal safety assessment. When present, herbal-induced liver injury (HILI) may limit the use or result in the discontinuation of these agents. HILI can exhibit with a wide spectrum of clinical and laboratory manifestations, ranging from asymptomatic elevations in aminotransferases to acute liver failure. Most cases of HILI resolve within several weeks after herbal remedy discontinuation. However, some cases can persist as low-level aminotransferase elevations. Our review aims to (1) describe the possible significant discrepancies between the ingredients listed on the label and the actual contents of the preparation; (2) evaluate teas containing multiple plants or herbs which may be adulterated by more toxic herbs, heavy metals, microbials, pharmaceuticals, and medicines; (3) describe pathophysiologic events in herbal tea-induced hepatotoxicity; and (4) discuss the key elements required for attributing the consumption of tea to the induction of liver injury. The widespread use of mixed heterogeneous remedies and the lack of randomized trials are an obstacle to providing safe use of plant-derived teas.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".