Spatial variations in ambient ultrafine particle concentrations and the risk of incident prostate cancer: A case-control study
Bibliographic record
Abstract
Background Diesel exhaust contains large numbers of ultrafine particles (UFPs, <0.1 µm) and is a recognized human carcinogen. However, epidemiological studies have yet to evaluate the relationship between UFPs and cancer incidence. Methods We conducted a case-control study of UFPs and incident prostate cancer in Montreal, Canada. Cases were identified from all main Francophone hospitals in the Montreal area between 2005 and 2009. Population controls were identified from provincial electoral lists of French Montreal residents and frequency-matched to cases using 5-year age groups. UFP exposures were estimated using a land use regression model. Exposures were assigned to residential locations at the time of diagnosis/recruitment as well as approximately 10-years earlier to consider potential latency between exposure and disease onset. Odds ratios (OR) and 95% confidence intervals (95% CI) were calculated per interquartile range (IQR) increase in UFPs (approximately 4000 particles/cm 3 ) using logistic regression models adjusting for individual-level and ecological covariates. Results Ambient UFP concentrations were associated with an increased risk of prostate cancer (OR=1.10, 95% CI: 1.01, 1.19) in fully adjusted models when exposures were assigned to residences 10-years prior to diagnosis. This risk estimate increased slightly (OR=1.17, 95% CI; 1.01, 1.35) when modeled as a non-linear natural spline function. A smaller increased risk (OR=1.04, 95% CI: 0.97, 1.11) was observed when exposures were assigned to residences at the time of diagnosis. Conclusions Exposure to ambient UFPs may increase the risk of prostate cancer. Future studies are needed to replicate this finding as this is the first study to evaluate this relationship.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".