Urinary Excretion of Phthalate Metabolites in School Children of China: Implication for Cumulative Risk Assessment of Phthalate Exposure
Bibliographic record
Abstract
We analyzed 13 metabolites of 9 phthalates in urine of 782 Chinese school children aged 8–11 years and estimated the daily intake for phthalates based on urinary metabolite levels. The daily intakes were compared with acceptable intake levels to calculate the hazard quotient (HQ) for single phthalate. Finally, the cumulative risk for each child was assessed by means of a hazard index (HI) which is the sum of HQs. Overall, 11 metabolites were found in at least 85% of the urine samples with the highest median concentration of 47.1 ng/mL (93.4 μg/g creatinine) for mono-n-butyl phthalate (MnBP). Monooctyl phthalate (MOP) and monoisononyl phthalate (MiNP) were not detectable. The cumulative risk assessment covering di(2-ethylhexyl) phthalate (DEHP), di-n-butyl phthalate (DnBP), di-isobutyl phthalate (DiBP), and butyl-benzyl phthalate (BBzP) demonstrated that 19.8% (volume model-based) and 40.3% (creatinine model-based) of the children exceeded 1 for the HI based on tolerable daily intake (TDI) values (considered as potential adverse antiandrogenic effect). Furthermore, at least 36% of the children from the manufacturing-intensive region had a HI higher than 1. The results indicate that Chinese children are widely exposed to phthalates and those from manufacturing-intensive regions are probably at a high risk of cumulative phthalate exposure.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".