Liver biomarkers in adults: Evaluation of associations with reported green tea consumption and use of green tea supplements in U.S. NHANES
Bibliographic record
Abstract
Some events of hepatotoxicity have been linked to consumption of green tea supplements. The association between consumption of green tea or green tea supplements and abnormal liver biomarkers in adults was investigated using cross-sectional data from the 2009-2014 United States National Health and Nutrition Examination Survey (U.S. NHANES). Individuals with levels of either bilirubin or GGT, ALT, AST, and/or ALP in excess of the age- and gender-specific upper limits of normal ranges were classified as having abnormal liver biomarkers. Associations between green tea or green tea supplement use (consumption vs. not) and liver function were determined using multiple logistic regression modelling. 12,289 persons were included in the green tea analyses and 12,274 in the green tea supplement analyses. The odds of having one or more abnormal liver biomarkers were significantly reduced (p = 0.01) with consumption of green tea (OR: 0.49; 95% CI: 0.28, 0.85), while no significant association (p = 0.78) was determined for consumption of green tea supplements (OR: 0.92; 95% CI: 0.52, 1.64). Based on data from the 2009-2014 U.S. NHANES, green tea consumption was associated with reduced odds of having one or more abnormal liver biomarkers; whereas, no significant association was determined with consumption of green tea 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".