Ambient polycyclic aromatic hydrocarbon exposure and breast cancer risk in a population-based Canadian case–control study
Bibliographic record
Abstract
PURPOSE: Polycyclic aromatic hydrocarbons (PAHs) represent a class of ubiquitous pollutants recognized as established human carcinogens and endocrine-disrupting chemicals. PAHs have seldom been modeled at the population-level in epidemiological studies. Fluoranthene is a prevalent PAH in urban settings and correlates with the occurrence of other PAHs. The purpose of this study was to evaluate associations between long-term residential exposure to ambient PAHs and breast cancer risk, both pre- and post-menopausal, in Canada. METHODS: Using the National Enhanced Cancer Surveillance System (NECSS), a national-scale Canadian population-based case-control study, annual fluoranthene exposures were estimated using the GEM-MACH-PAH chemical transport model on the basis of geocoded residential histories throughout a 20-year exposure window. Odds ratios (ORs) and 95% confidence intervals (CIs) controlling for potential confounders were estimated using logistic regression. Separate analyses were conducted for Ontario and national samples given a finer-resolution exposure surface and additional risk factor information available for Ontario. RESULTS: Positive associations were observed between fluoranthene exposure and premenopausal breast cancer, with inconsistent findings for postmenopausal breast cancer. For premenopausal breast cancer, adjusted ORs of 2.48 (95% CI: 1.29, 4.77) and 1.59 (95% CI: 1.11, 2.29) were observed when comparing the second highest category of exposure to the lowest, among the Ontario and national samples, respectively. For postmenopausal breast cancer, adjusted ORs were 1.10 (95% CI: 0.67, 1.80) and 1.33 (95% CI: 1.02, 1.73). Associations for the highest level of exposure, across both samples and menopausal strata, were non-significant. CONCLUSION: This study provides support for the hypothesis that ambient PAH exposures 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".