Urinary excretion and daily intake rates of diethyl phthalate in the general Canadian population
Bibliographic record
Abstract
We have analyzed the trends in the body-weight-adjusted urinary monoethyl phthalate (MEP) concentrations and the diethyl ethyl phthalate (DEP) daily intake estimates in the general Canadian population (aged 6-49 years) using the Canadian Health Measures Survey 2007-2009 dataset. The creatinine correction approach, as well as the urine volume approach in a simple one compartment model were used to calculate the daily urinary MEP excretion rates and DEP intake rates in individual survey participants. Using multiple regression models, we have estimated least square geometric means (LSGMs) of body-weight-adjusted MEP concentration, daily excretion and intake rates among different age groups and sex. We observed that body weight affects the trends in the MEP concentrations significantly among children (aged 6-11 years), adolescents (aged 12-19 years) and adults (aged 20-49 years). The body-weight-adjusted MEP concentrations in children were significantly higher than those in adults. On the other hand the DEP daily intakes in children were significantly lower than those in adults. We did not observe any differences in the DEP daily intake rates between males and females. Although the urinary MEP concentrations are correlated well with DEP daily intake estimates in the overall population, one should be cautious when directly using the urinary concentrations to compare the intake trends in the sub-populations (e.g. children vs. adults) as these trends are governed by additional physiological factors. The DEP daily intake calculated using the creatinine approach and that using the urine volume approach were similar to each other. The estimated geometric mean and 95th percentile of DEP daily intake in the general Canadian population are 2 and 20 μg/kg-bw/day, respectively. These daily intake estimates are significantly lower than the US Environmental Protection Agency's oral reference dose of 800 μg/kg-bw/day.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".