Gestational and early childhood air pollution exposure and neurodevelopmental outcomes in early childhood
Bibliographic record
Abstract
Background Exposure to ambient air pollution, including fine particulate matter (PM 2.5 ), nitrogen dioxide (NO 2 ), and elemental carbon, has been associated with worse neurodevelopmental outcomes in children, but research on early childhood outcomes is limited. Objective This study examined the associations between gestational and early childhood exposures to PM 2.5 and NO 2 and child developmental outcomes measured at ages 1, 2, and 3 years. Methods In the Health Outcomes and Measures of the Environment (HOME) Study, a longitudinal pregnancy and birth cohort, we used spatiotemporal models to estimate the concentration of each air pollutant at participants' home addresses during early brain development. Neurodevelopmental outcomes were measured using the Bayley Scales of Infant Development, Second Edition (BSID-II) at ages 1, 2, and 3 years. For 329 children, we examined the associations of air pollution with BSID-II scores using generalized linear models with generalized estimating equations (GEE). Results Gestational and early childhood NO 2 concentrations were positively associated with cognitive development at age 1 year but negatively associated with cognitive development at age 3 years. Similarly, PM 2.5 exposure through age 3 years was negatively associated with cognitive development at age 3 years. Gestational NO 2 concentration was positively associated with motor development at age 1 year but negatively associated with motor development at ages 2 and 3 years. Discussion These results suggest that exposure to traffic-related air pollution during gestation and after birth may impact neurodevelopment in early childhood.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".