Exposure to ambient polycyclic aromatic hydrocarbons and early-onset female breast cancer in a case–control study in Ontario, Canada
Bibliographic record
Abstract
Background: Ambient polycyclic aromatic hydrocarbons (PAHs) are a class of toxicologically important and understudied air pollutants. Epidemiologic evidence suggests that chronic exposure to PAHs increases breast cancer risk; however, there are few studies in nonoccupational settings that focus on early-onset diagnoses. Methods: The relationship between residentially-based ambient PAH concentrations and female breast cancer, among those 18-45 years of age, was characterized in the Ontario Environment and Health Study (OEHS). The OEHS was a population-based case-control study undertaken in Ontario, Canada between 2013 and 2015. Primary incident breast cancers were identified within 3 months of diagnosis, and a population-based series of controls were recruited. Concentrations of ambient PAHs, using fluoranthene as a surrogate, were derived using a chemical transport model at a 2.5 km spatial resolution. These estimates were assigned to participants' residences at the time of the interview and 5 years prior. Logistic regression was used to estimate odds ratios (ORs) and their 95% confidence intervals (CIs) based on a quartile categorization of fluoranthene exposure while adjusting for a series of individual- and area-level risk factors. The shape of the exposure-response trend was evaluated using cubic splines. Results: , respectively. In models adjusted for a parsimonious set of risk factors, the highest quartile of exposure was associated with an increased risk of breast cancer (OR = 2.16; 95% CI = 1.22, 3.84). Restricted spline analyses revealed nonlinear dose-response patterns. Conclusions: These findings support the hypothesis that ambient PAH exposures increases the risk of early-onset 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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".