A Simple Pharmacokinetic Model of Prenatal and Postnatal Exposure to Perfluoroalkyl Substances (PFASs)
Bibliographic record
Abstract
Most children are exposed to perfluoroalkyl substances (PFASs) through placental transfer, breastfeeding, and other environmental sources. To date, there are no validated tools to estimate exposure and body burden during infancy and childhood. In this study, we aimed to (i) develop a two-generation pharmacokinetic model of prenatal and postnatal exposure to perfluorooctanoic acid (PFOA), perfluorooctanesulfonate (PFOS), and perfluorohexanesulfonate (PFHxS); and to (ii) evaluate it against measured children's levels in two studies. We developed a pharmacokinetic model consisting of a maternal and a child compartment to simulate lifetime exposure in women and transfer to the child across the placenta and through breastfeeding. To evaluate the model, we performed simulations for each mother-child dyad from two studies in which maternal PFAS levels at delivery and children's PFAS levels were available. Model predictions based on maternal PFAS levels, sex of child, body weight, and duration of breastfeeding explained between 52% and 60% of the variability in measured children's levels at 6 months of age and between 52% and 62% at 36 months. Monte Carlo simulations showed that the daily intake through breastfeeding and resulting internal PFAS levels can be much higher in nursing infants than in mothers. This pharmacokinetic model shows potential for postnatal exposure assessment in the context of epidemiological studies and risk assessment.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".