Patterns, Variability, and Predictors of Urinary Bisphenol A Concentrations during Childhood
Bibliographic record
Abstract
We examined the patterns, variability, and predictors of urinary bisphenol A (BPA) concentrations in 337 children from the Cincinnati, Ohio HOME Study. From 2003 to 2014, we collected two urine samples from women at 16 and 26 weeks of pregnancy and six urine samples from children at 1-5 and 8 years of age. We used linear mixed models to calculate intraclass correlation coefficients (ICCs) as a measure of within-person BPA variability and to identify sociodemographic and environmental predictors. For the 8-year visit, we used multivariable linear regression to explore associations between urinary BPA concentrations and exposure-related factors. We calculated daily intakes using equations estimating creatinine excretion rates and creatinine-standardized BPA concentrations. Urinary BPA concentrations, which decreased over childhood, had a low degree of reproducibility (ICC < 0.2). Estimated daily intakes decreased with age and were below the reference dose of 50 μg/kg body weight/day. BPA concentrations were positively associated with consuming food stored or heated in plastic, consuming canned food and beverages, and handling cash register receipts. Our results suggest that there are multiple sources of BPA exposure in young children. Etiological studies should collect serial urine samples to accurately classify BPA exposure and consider sociodemographic and environmental factors as possible confounders.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".