Traffic-related air pollution and prostate cancer risk: a case–control study in Montreal, Canada
Bibliographic record
Abstract
OBJECTIVES: There is a paucity of information on environmental risk factors for prostate cancer. We conducted a case-control study in Montreal to estimate associations with exposure to ground-level nitrogen dioxide (NO2), a marker for traffic-related air pollution. METHODS: Cases were 803 men with incident prostate cancer, ≤75 years of age, and diagnosed across all French hospitals in Montreal. Concurrently, 969 controls were drawn from electoral lists of French-speaking individuals residing in the same electoral districts as the cases and frequency-matched by age. Concentrations of NO2 were measured across Montreal in 2005-2006. We developed a land use regression model to predict concentrations of NO2 across Montreal for 2006. These estimates were back-extrapolated to 1996. Estimates were linked to residential addresses at the time of diagnosis or interview. Unconditional logistic regression was used, adjusting for potential confounding variables. RESULTS: For each increase of 5 parts per billion of NO2, as estimated from the original land use regression model in 2006, the OR5ppb adjusted for personal factors was 1.44 (95% CI 1.21 to 1.73). Adding in contextual factors attenuated the OR5ppb to 1.27 (95% CI 1.03 to 1.58). One method for back-extrapolating concentrations of NO2 to 1996 (about 10 years before the index date) gave the following OR5ppb: 1.41 (95% CI 1.24 to 1.62) when personal factors were included, and 1.30 (95% CI 1.11 to 1.52) when contextual factors were added. CONCLUSIONS: Exposure to ambient concentrations of NO2 at the current address was associated with an increased risk of prostate cancer. This novel finding requires replication.
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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 | 0.000 | 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) | 0.002 | 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 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".