Traffic Related Air Pollution and Lung Function in Bilateral Lung Transplant Patients
Bibliographic record
Abstract
Lung transplants improve the quality of life for individuals with end-stage lung disease.International annual rates of bilateral lung transplants have been increasing since 1990.Forced expiratory volume (FEV 1 ) is used to evaluate lung function and disease diagnosis pre-transplant and prognosis post-transplant.Recent studies suggest that traffic related air pollution (TRAP) decreases lung function, but few studies have evaluated this association in lung transplant patients.Therefore, we evaluated this cross-sectional relationship in bilateral lung transplant patients (N=361) in the 2002-2012 Toronto General Hospital cohort.TRAP was evaluated as length and density of major roads around patients' residential addresses using 2013 ArcGIS.Within buffer total major road length was dichotomized at 100m, 300m and 500m.Maximum major road density categorization includes <100m, 100m-300m, 300m-700m and >700m.FEV 1 was measured as part of routine post-transplant care within the first year; clinical information was taken from medical records.Multiple linear and logistic regressions were used to evaluate maximum FEV 1 and percent predicted FEV 1 (PP FEV 1 ) against TRAP, adjusting for clinical factors.Maximum major road density within 100m from residential address was associated with 9.67% (95% Confidence Interval [CI], -15.59, -3.75) lower PP FEV 1 and 0.38L (95% CI, -0.59, -0.17) lower maximum FEV 1 compared with more than 700m.Each 57m increase in major road length within 100m from residence was associated with a 1.55% (95% CI, -2.75, -0.34) decrease in PP FEV 1 and 0.06L (95% CI, -0.09, -0.01) decrease in maximum FEV 1 .Our findings show that reducing TRAP may provide better outcomes for vulnerable populations.vi
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".