Exposure to air pollutants and subclinical carotid atherosclerosis measured by magnetic resonance imaging: A cross-sectional analysis
Bibliographic record
Abstract
OBJECTIVES: Long-term exposure to air pollution has been associated with higher risk of cardiovascular mortality. Less is known about the association of air pollution with initial development of cardiovascular disease. Herein, the association between low-level exposure to air pollutants and subclinical carotid atherosclerosis in adults without known clinical cardiovascular disease was investigated. DESIGN: Cross-sectional analysis within a prospective cohort study. SETTING: The Canadian Alliance for Healthy Hearts and Minds Cohort Study; a pan-Canadian cohort of cohorts. PARTICIPANTS: Canadian adults (n = 6645) recruited between 2014-2018 from the provinces of British Columbia, Alberta, Ontario, Quebec, and Nova Scotia, were studied, for whom averages of exposures to nitrogen dioxide (NO2), ozone (O3), and fine particulate matter (PM2.5) were estimated for the years 2008-2012. MAIN OUTCOME MEASURE: Carotid vessel wall volume (CWV) measured by magnetic resonance imaging (MRI). RESULTS: In adjusted linear mixed models, PM2.5 was not consistently associated with CWV (per 5 μg/m3 PM2.5; adjusted estimate = -8.4 mm3; 95% Confidence Intervals (CI) -23.3 to 6.48; p = 0.27). A 5 ppb higher NO2 concentration was associated with 11.8 mm3 lower CWV (95% CI -16.2 to -7.31; p<0.0001). A 3 ppb increase in O3 was associated with 9.34 mm3 higher CWV (95% CI 4.75 to 13.92; p<0.0001). However, the coarse/insufficient O3 resolution (10 km) is a limitation. CONCLUSIONS: In a cohort of healthy Canadian adults there was no consistent association between PM2.5 or NO2 and increased CWV as a measure of subclinical atherosclerosis by MRI. The reasons for these inconsistent associations warrant further study.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".