Ambient Air Pollution and Dysanapsis: Associations with Lung Function and Chronic Obstructive Pulmonary Disease in the Canadian Cohort Obstructive Lung Disease Study
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Bibliographic record
Abstract
Rationale: Outdoor air pollution is a potential risk factor for lower lung function and chronic obstructive pulmonary disease (COPD). Little is known about how airway abnormalities and lung growth might modify this relationship. Objectives: To evaluate the associations of ambient air pollution exposure with lung function and COPD and examine possible interactions with dysanapsis. Methods: We made use of cross-sectional postbronchodilator spirometry data from 1,452 individuals enrolled in the CanCOLD (Canadian Cohort Obstructive Lung Disease) study with linked ambient fine particulate matter (PM2.5) and nitrogen dioxide (NO2) air pollution estimates. Dysanapsis, or the ratio of the airway-to-lung volume calculated from thoracic computed tomography images, was used to examine possible interactions. Measurements and Main Results: In adjusted models, 101.7 ml (95% confidence interval [CI], -166.2 to -37.2) and 115.0 ml (95% CI, -196.5 to -33.4) lower FEV1 were demonstrated per increase of 2.4 ug/m3 PM2.5 and 9.2 ppb NO2, respectively. Interaction between air pollution and dysanapsis was not statistically significant when modeling the airway-to-lung ratio as a continuous variable. However, a 109.8 ml (95% CI, -209.0 to -10.5] lower FEV1 and an 87% (95% CI, 12% to 213%) higher odds of COPD were observed among individuals in the lowest, relative to highest, airway-to-lung ratio, per 2.4 μg/m3 increment of PM2.5. Conclusions: Ambient air pollution exposure was associated with lower lung function, even at relatively low concentrations. Individuals with dysanaptic lung growth might be particularly susceptible to inhaled ambient air pollutants, especially those at the extremes of dysanapsis. Keywords: air quality; chronic airflow obstruction; chronic obstructive pulmonary disease; computed tomography; pulmonary function test.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it