Long‐term exposure to fine particulate matter air pollution and the risk of lung cancer among participants of the Canadian National Breast Screening Study
Bibliographic record
Abstract
Recently, air pollution has been classified as a carcinogen largely on the evidence of epidemiological studies of lung cancer. However, there have been few prospective studies that have evaluated associations between fine particulate matter (PM2.5 ) and cancer at lower concentrations. We conducted a prospective analysis of 89,234 women enrolled in the Canadian National Breast Screening Study between 1980 and 1985, and for whom residential measures of PM2.5 could be assigned. The cohort was linked to the Canadian Cancer Registry to identify incident lung cancers through 2004. Surface PM2.5 concentrations were estimated using satellite data. Cox proportional hazards models were used to characterize associations between PM2.5 and lung cancer. Hazard ratios (HRs) and 95% confidence intervals (CIs) computed from these models were adjusted for several individual-level characteristics, including smoking. The cohort was composed predominantly of Canadian-born (82%), married (80%) women with a median PM2.5 exposure of 9.1 µg/m(3) . In total, 932 participants developed lung cancer. In fully adjusted models, a 10 µg/m(3) increase in PM2.5 was associated with an elevated risk of lung cancer (HR: 1.34; 95% CI = 1.10, 1.65). The strongest associations were observed with small cell carcinoma (HR: 1.53; 95% CI = 0.93, 2.53) and adenocarcinoma (HR: 1.44; 95% CI = 1.06, 1.97). Stratified analyses suggested increased PM2.5 risks were limited to those who smoked cigarettes. Our findings are consistent with previous epidemiological investigations of long-term exposure to PM2.5 and lung cancer. Importantly, they suggest associations persist at lower concentrations such as those currently found in Canadian cities.
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".