Association of Blood Mercury Level with Liver Enzymes in Korean Adults: An Analysis of 2015–2017 Korean National Environmental Health Survey
Bibliographic record
Abstract
Increased liver enzymes as a result of exposure to mercury and their toxic effects are not well understood in Korea at the population level. The effect of blood mercury concentration on alanine aminotransferase (ALT) and aspartate aminotransferase (AST) was evaluated after adjusting for sex, age, obesity, alcohol consumption habit, smoking, and exercise parameters in 3712 adults. The risk of abnormal liver function was measured using a multiple logistic regression analysis. Blood mercury concentration was divided into quartiles, and liver enzyme levels were compared for each quartile. ALT and AST levels were 10-20% higher in the second, third, and fourth quartiles compared to the first quartile. The risk of liver dysfunction or elevated liver enzymes was significantly higher in the second, third, and fourth quartiles than in the first quartile. As blood mercury levels increased, liver enzymes and mercury-induced hepatotoxicity increased. The increase in liver enzymes caused by mercury was more pronounced in the low-mercury concentration range. To reduce the long-standing problem of abnormal liver enzymes and liver function in Korea and other similar settings, it is important to decrease exposure to mercury through effective implementation of specific health and environmental strategies.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".