Bisphenol A and phthalate metabolite urinary concentrations: Daily and across pregnancy variability
Bibliographic record
Abstract
Phthalates and bisphenol A (BPA) are high production volume and ubiquitous chemicals that are quickly metabolized in the body. Traditionally, studies have relied on single spot urine analyses to assess exposure; ignoring variability in concentrations throughout a day or over a longer period of time. We compared BPA and phthalate metabolite results from urine samples collected at five different time points. Participants (n=80) were asked to collect all voids in a 24 h period on a weekday and then again on a weekend before 20 weeks of pregnancy. During the second and third trimesters and in the postpartum period, single spot urines were collected. Variability over time in urinary concentrations was assessed using intraclass correlation coefficients (ICCs) and the sensitivity to correctly classify a single sample as high or low versus the geometric mean (GM) of all samples was calculated. We found low reproducibility and sensitivity of BPA and all phthalate metabolites throughout pregnancy and into the postpartum period but much higher reproducibility within a day. Time of day when the urine was collected was a significant predictor of specific gravity adjusted exposure levels. We concluded that, if the interest is in average exposures across pregnancy, maternal/fetal exposure estimation may be more accurate if multiple measurements, collected across the course of the entire pregnancy, rather than a single spot measure, are performed.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".