Representative levels of blood lead, mercury, and urinary cadmium in youth: Korean Environmental Health Survey in Children and Adolescents (KorEHS-C), 2012–2014
Bibliographic record
Abstract
BACKGROUND: This study examined levels of blood lead and mercury, and urinary cadmium, and associated sociodemographic factors in 3-18 year-old Korean children and adolescents. MATERIALS AND METHODS: We used the nationally representative Korean Environmental Health Survey in Children and Adolescents data for 2012-2014 and identified 2388 children and adolescents aged 3-18 years. The median and 95th percentile exposure biomarker levels with 95% confidence intervals (CIs) were calculated. Multivariate regression analyses were performed on log transformed exposure biomarker levels adjusted for age, sex, area, household income, and father's education level. The median exposure biomarker levels were compared with data from Germany, the US, and Canada, as well as the levels of Korean children measured at different times. RESULTS: The median levels of blood lead and mercury, as well as urinary cadmium were 1.23μg/dL, 1.80μg/L, and 0.40μg/L (95% CIs, 1.21-1.25, 1.77-1.83, and 0.39-0.41, respectively). The blood lead levels were significantly higher in boys and younger children (p<0.0001) and children with less educated fathers (p=0.004) after adjusting for covariates. Urinary cadmium level increased with age (p<0.0001). The median levels of blood mercury and urinary cadmium were much higher in Korean children and adolescents than those in their peers in Germany, the US, and Canada. Blood lead levels tended to decrease with increasing age and divergence between the sexes, particularly in the early teen years. Median levels of blood lead and urinary cadmium decreased since 2010. CONCLUSION: Sociodemographic factors, including age, sex, and father's education level were associated with environmental exposure to heavy metals in Korean children and adolescents. These biomonitoring data are valuable for ongoing surveillance of environmental exposure in this vulnerable population.
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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 | 0.001 |
| 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".