Associations between prenatal exposure to a mixture of lead, mercury and polychlorinated biphenyls and executive function in Inuit adolescents
Bibliographic record
Abstract
Numerous studies have investigated the individual contribution of prenatal exposure to lead (Pb), mercury (Hg) and polychlorinated biphenyls (PCBs) on neurocognitive development, but few have explored their combined influence, particularly on executive function, during adolescence. This study aims to assess the associations between prenatal exposure to Pb, Hg and PCB-153 and executive function in mid-to-late adolescence. Two-hundred twelve Inuit participants (mean age = 18.5 years, range = 16.0 to 21.9) from Nunavik, Canada, completed four tasks assessing executive function: a Stop Task, a 2n-back task, the D-KEFS Trail Making Test and the Tower of London. Exposure to Pb, Hg and PCB-153 was estimated in cord blood samples at birth, and in blood samples at 11 years old and at time of testing. Bayesian Kernel Machine Regression and traditional multiple linear regression models were performed to estimate mixture and individual effects. All models were adjusted for sociodemographic characteristics and fish nutriments as well as for postnatal contaminant exposure in secondary analyses. Mixture modeling of concurrent prenatal exposure to Pb, Hg and PCB-153 did not reveal any statistically significant associations with executive function. However, results from the single-pollutant regression models showed a negative log-linear relationship between cord Pb concentrations and cognitive planning, which remained statistically significant after controlling for postnatal exposure (β = -0.178, 95 % CI = [-0.327, -0.021], p = 0.021). This study suggests that prenatal exposure to Pb is detrimental to executive function in late adolescence. Further research is needed to replicate findings and better understand the functional significance of this long-lasting association and how it might evolve during adulthood.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".