Proximity to Major Roadways is a Risk Factor for Airway Hyper-Responsiveness in Adults
Bibliographic record
Abstract
BACKGROUND: Proximity to major roads is reported to be associated with asthma and airway hyper-responsiveness in children. Similar studies using objective measurements in adults are not available in Canada. OBJECTIVE: To test the hypothesis that adult asthmatic patients who live close to major roads and highways in an urban environment are at a risk of moderate to severe airway hyper-responsiveness. METHODS: Airway responsiveness was determined using methacholine bronchial provocation (PC(20)) tests in a cohort of 2625 patients who attended an outpatient clinic in Hamilton, Ontario. Patient addresses were geocoded in a geographic information system to determine proximity to major roads and highways. Multivariate linear and multinomial regression analyses were used to assess whether proximity to roads was a risk factor for airway hyper-responsiveness as measured by PC(20) methacholine. RESULTS: Patients who lived within 200 m of a major road had increased odds (OR 1.38 [95% CI 1.04 to 1.85]) of having moderate airway hyperresponsiveness (0.25 mg⁄mL 16 mg/mL). Spatial analysis also revealed that the majority of patients with severe airway hyper-responsiveness lived within the urban core of the city while those with moderate to mild hyper-responsiveness were also dispersed in rural areas. CONCLUSIONS: In an adult population of patients attending an outpatient respiratory clinic in Hamilton, living close to major roadways was associated with an increased risk of moderate airway hyper-responsiveness. This correlation suggests that exposure to traffic emissions may provoke the pathology of airway hyper-responsiveness leading to variable airflow obstruction.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".