Residential exposure to fine particulate matter air pollution and incident breast cancer in a cohort of Canadian women
Bibliographic record
Abstract
Background: Air pollution has been classified as a human carcinogen based largely on epidemiological studies of lung cancer. Recent research suggests that exposure to ambient air pollution increases the risk of breast cancer. Methods: Our aim was to characterize associations between residential exposure to ambient fine particulate matter (PM2.5) and the incidence of breast cancer in a cohort of 89,247 women enrolled in the Canadian National Breast Screening Study between 1980 and 1985. Vital status and incident cancers were determined through record linkage to the national registry data through 2005. Individual-level estimates of exposures to PM2.5 at baseline were derived from satellite observations. Six thousand five hundred three incident breast cancers were identified during follow-up. We classified menopausal status using self-reported information collected at baseline and by attained age (50, 52, and 54 years) as women were followed-up. We computed hazard ratios (HRs) and their 95% confidence intervals (CI) using age as the time axis. Models were adjusted for several individual risk factors, including reproductive history, as well as census-based neighborhood-level characteristics. Results: The median residential concentration of PM2.5 was 9.1 μg/m3. In models adjusted for personal and contextual risk factors, a 10-μg/m3 increase in PM2.5 was associated with an elevated risk of premenopausal (HR = 1.26; 95% CI = 0.99, 1.61) but not postmenopausal breast cancer (HR = 1.01; 95% CI = 0.94, 1.10). The elevated risk of premenopausal breast cancer from PM2.5 was only evident among those randomized to the screening arm of the study. Conclusions: Our findings support the hypothesis that exposure to low concentrations of PM2.5 increase the risk of premenopausal breast cancer.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".