VALIDATING A PHARMACOKINETIC MODEL OF POLYCHLORINATED BIPHENYLS (PCBs) USING BLOOD LEVELS IN CHILDREN FROM BIRTH UP TO 22 MONTHS OF AGE
Bibliographic record
Abstract
Background and aims: Recent evidence suggests that not only can PCBs disrupt neurodevelopment following prenatal exposure, but also that there are postnatal windows of susceptibility during which exposure may impact additional processes. Estimation of children exposure during different time windows is crucial when assessing such chemically induced ailments. However, the validation of a previously developed pharmacokinetic tool was restricted to the first 6 months of life. We aimed at validating a physiologically based pharmacokinetic (PBPK) model for the postnatal exposure to PCBs using blood levels sampled up to 22 months of age in a birth cohort of children from Slovakia. Methods: We simulated blood PCB-153 level profiles in 328 children with sufficient information on cord blood levels, weight and height at or close to blood sampling times, duration of breast-feeding, gender and blood levels at two time points (on average at 6 and 16 months, ranging from 5 to 22 months). Predictions were compared to measured levels through Spearman’s rank correlations and linear regression. Results: Estimated levels correlated to measured levels with Spearman correlation coefficients of 0.79 and 0.81 for the 6 month and 16 month sampling times, respectively. Linear regression analyses revealed that estimations explained 62 % and 63 % of the variability in measured levels at 6 and 16 months. Conclusions: Although correlations in this study were slightly lower than that obtained in our previous study, results suggest that the model is suitable for the estimation of postnatal levels up to 22 months of age. Studies are underway to calibrate the model, extend its validation to children of 5 years of age and to investigate the impact of postnatal exposure to PCBs on a multitude of health outcomes.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".