Association between nitrogen dioxide and incident breast cancer in Alberta’s tomorrow project
Bibliographic record
Abstract
Abstract Breast cancer is a significant global health concern, with millions of cases diagnosed annually. Risk factors for breast cancer include obesity, physical inactivity, poor diet, and alcohol use. Urbanization introduces exposures such as traffic-related air pollution (TRAP), which may impact breast cancer risk. To assess the impact of TRAP, measured in this study using nitrogen dioxide (NO2), on the risk of post-menopausal breast cancer we conducted a secondary analysis of baseline and follow-up data from the Alberta Tomorrow Project (ATP) cohort, linked to geographic data at the postal code level from the Canadian Urban Environmental Health Research Consortium (CANUE). Cox proportional hazard regression models were built to assess the association between breast cancer risk and NO2. Direct acyclic graphs (DAG) were used to identify and select relevant confounders for adjustment. The study included 15,536 post-menopausal women and 523 incident breast cancer cases over 12.6 years of follow-up. The hazard ratio (HR) for the initial model, which included all potential confounders (age, education, number of births, fruits and vegetables consumption, income, physical activity, BMI, and smoking) was 1.01 per 10-ppb increase in NO2. The adjusted HR for a 10-ppb increase in NO2 measured at baseline (the year participants completed the health and behaviours survey) was 1.10 (95% CI = 0.90, 1.34). Our study found no significant association between NO2 exposure and post-menopausal breast cancer risk. The variation in reported hazard ratios (HRs) — ranging from 1.01 to 1.10 — reflects different models adjusting for different variables. Our findings suggest a risk magnitude similar to other studies, yet the lack of significant association may be due to sample size limitations and lower NO2 levels in Alberta compared to other regions globally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".