Fine Particulate Matter Air Pollution, Proximity to Traffic, and Aortic Atherosclerosis
Bibliographic record
Abstract
BACKGROUND: The initiation and acceleration of atherosclerosis is hypothesized as a physiologic mechanism underlying associations between air pollution and cardiovascular effects. Despite toxicologic evidence, epidemiologic data are limited. METHODS: In this cross-sectional analysis we investigated exposure to fine particulate matter (PM2.5) and residential proximity to major roads in relation to abdominal aortic calcification, a sensitive indicator of systemic atherosclerosis. Aortic calcification was measured by computed tomography among 1147 persons, in 5 US metropolitan areas, enrolled in the Multi-Ethnic Study of Atherosclerosis. The presence and quantity of aortic calcification were modeled using relative risk regression and linear regression, respectively, with adjustment for potential confounders. RESULTS: We observed a slightly elevated risk of aortic calcification (RR = 1.06; 95% confidence interval = 0.96-1.16) with a 10 microg/m contrast in PM2.5. The PM2.5-associated risk of aortic calcification was stronger among participants with long-term residence near a PM2.5 monitor (RR = 1.11; 1.00-1.24) and among participants not recently employed outside the home (RR = 1.10; 1.00-1.22). PM2.5 was not associated with an increase in the quantity of aortic calcification (Agatston score) and no roadway proximity effects were noted. There was indication of PM2.5 effect modification by lipid-lowering medication use, with greater effects among users, and PM2.5 associations were observed most consistently among Hispanics. CONCLUSIONS: Although we did not find persuasive associations across our full study population, associations were stronger among participants with less exposure misclassification. These findings support the hypothesis of a relationship between particulate air pollution and systemic atherosclerosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".