The Influence of Living Near Roadways on Spirometry and Exhaled Nitric Oxide in Elementary Schoolchildren
Bibliographic record
Abstract
BACKGROUND: Living near major roadways has been associated with an increase in respiratory symptoms, but little is known about how this relates to airway inflammation. OBJECTIVE: We assessed the effects of living near local residential roadways based on objective indicators of ventilatory function and airway inflammation. METHODS: We estimated ambient air pollution, resolved to the level of the child's neighborhood, using a land-use regression model for children 9-11 years of age. We also summed the length of roadways found within a 200-m radius of each child's neighborhood. We had measurements of both air pollution exposure and spirometry for 2,328 children, and also had measurements of exhaled nitric oxide (eNO) for 1,613 of these children. RESULTS: Each kilometer of local roadway within a 200-m radius of the home was associated with a 6.8% increase in eNO (p = 0.045). Each kilometer of any type of roadway (local, major, highway) was also associated with an increase in eNO of 10.1% (p = 0.002). Each microgram per cubic meter increase in PM2.5 was associated with a 3.9% increase in eNO (p = 0.058) and 0.70% decrease in forced vital capacity (FVC) expressed as a percentage of predicted (p = 0.39). Associations between roadway density and both forced expired volume in 1 sec and FVC were negative but not statistically significant at p < 0.05. CONCLUSION: Traffic from local neighborhood roadways may cause airway inflammation as indicated by eNO. This may be a more sensitive indicator of adverse air pollution effects than traditional measures of ventilatory function.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".