Traffic exposures, air pollution and outcomes in pulmonary arterial hypertension: a UK cohort study analysis
Bibliographic record
Abstract
While traffic and air pollution exposure is associated with increased mortality in numerous diseases, its association with disease severity and outcomes in pulmonary arterial hypertension (PAH) remains unknown. Exposure to particulate matter with a 50% cut-off aerodynamic diameter ≤2.5 μm (PM 2.5 ), nitrogen dioxide (NO 2 ) and indirect measures of traffic-related air pollution (distance to main road and length of roads within buffer zones surrounding residential addresses) were estimated for 301 patients with idiopathic/heritable PAH recruited in the UK National Cohort Study of Idiopathic and Heritable PAH. Associations with transplant-free survival and pulmonary haemodynamic severity at baseline were assessed, adjusting for confounding variables defined a priori . Higher estimated exposure to PM 2.5 was associated with higher risk of death or lung transplant (unadjusted hazard ratio (HR) 2.68 (95% CI 1.11–6.47) per 3 μg·m −3 ; p=0.028). This association remained similar when adjusted for potential confounding variables (HR 4.38 (95% CI 1.44–13.36) per 3 μg·m −3 ; p=0.009). No associations were found between NO 2 exposure or other traffic pollution indicators and transplant-free survival. Conversely, indirect measures of exposure to traffic-related air pollution within the 500–1000 m buffer zones correlated with the European Society of Cardiology/European Respiratory Society risk categories as well as pulmonary haemodynamics at baseline. This association was strongest for pulmonary vascular resistance. In idiopathic/heritable PAH, indirect measures of exposure to traffic-related air pollution were associated with disease severity at baseline, whereas higher PM 2.5 exposure may independently predict shorter transplant-free survival.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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".