Traffic-related air pollution is related to interrupter resistance in 4-year-old children
Bibliographic record
Abstract
Outdoor air pollution has been associated with decrements in lung function and growth of lung function in school-age children. Lung function effects have not been examined in preschoolers, with the exception of one study on minute ventilation in newborns. Our goal was to assess the relationship between long- and short-term exposure to traffic-related air pollution and interrupter resistance in 4-year-old children. Lung function was measured using the interrupter resistance method in children participating in a Dutch birth cohort study. Long-term average air pollution concentrations of fine particulate matter, nitrogen dioxide and soot at the residential address at birth were assessed using land-use regression models. Daily average air pollution concentrations on the day of clinical examination were obtained from the Dutch National Air Quality Monitoring Network. Significant associations were found between long-term average air pollution concentrations and interrupter resistance. Interrupter resistance increased by 0.04 kPa·s·L(-1) (95% CI 0.01-0.07) per interquartile range increase (3.3 μg·m(-3)) in fine particle concentration. Short-term exposure was not associated with interrupter resistance. Long-term exposure to traffic-related air pollution was associated with increased interrupter resistance in 4-year-old children, supporting previous birth cohort studies reporting effects of air pollution on subjectively reported respiratory symptoms in preschool children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".