Lifetime Exposure to Ambient Pollution and Lung Function in Children
Bibliographic record
Abstract
RATIONALE: Few studies have examined associations between exposure to air pollution and childhood lung function after implementation of strict air quality regulations in the 1990s. OBJECTIVES: To assess traffic-related pollution exposure and childhood lung function. METHODS: We geocoded addresses for 614 mother-child pairs enrolled during pregnancy in the Boston area 1999-2002 and followed them until a mid-childhood visit (median age, 7.7). We calculated the proximity of the home to the nearest major roadway. We estimated first year of life, lifetime, and prior-year exposure to particulate matter with a diameter smaller than 2.5 μm (PM2.5) by a hybrid model using satellite-derived aerosol optical depth, and to black carbon (BC) by a land-use regression model. MEASUREMENTS AND MAIN RESULTS: Residential proximity to roadway and prior-year and lifetime PM2.5 and BC exposure were all associated with lower FVC. Associations with FEV1 were also negative and proportionally similar. Pollution exposures were not associated with the FEV1/FVC ratio or bronchodilator response. Compared with distances greater than or equal to 400 m, living less than 100 m from a major roadway was associated with lower FVC (-98.6 ml; -176.3 to -21.0). Each 2 μg/m(3) increment in prior-year PM2.5 was associated with lower FVC (-21.8 ml; -43.9 to 0.2) and higher odds of FEV1 less than 80% predicted (1.41; 1.03-1.93). Each 0.2 μg/m(3) increment in prior-year BC was associated with a 38.9 ml (-70.4 to -7.3) lower FVC. CONCLUSIONS: Estimates of long-term exposure to ambient pollution, including proximity to major roadway, PM2.5, and BC (a traffic-related PM2.5 constituent), were associated with lower lung function in this Boston-area cohort of children with relatively low pollution exposures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".