Ambient air pollution and incident bladder cancer risk: Updated analysis of the Spanish Bladder Cancer Study
Bibliographic record
Abstract
Although outdoor air pollution and particulate matter in outdoor air have been consistently linked with increased lung cancer risk, the evidence for associations at other cancer sites is limited. Bladder cancer shares several risk factors with lung cancer and some positive associations of ambient air pollution and bladder cancer risk have been observed. This study examined associations of ambient air pollution and bladder cancer risk in the large‐scale Spanish Bladder Cancer Study. Estimates of ambient fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations were assigned to the geocoded participant residence of 938 incident bladder cancer cases and 973 hospital controls based on European multicity land‐use regression models. Adjusted odds ratios (ORs) and 95% confidence intervals (CI) for associations of ambient air pollution and bladder cancer risk were estimated using unconditional logistic regression models. Overall, there was no clear association between either ambient PM2.5 (OR per 5.9 μg/m3 = 1.06, 95% CI 0.71–1.60) or NO2 (OR per 14.2 μg/m3 = 0.97, 95% CI 0.84–1.13) concentrations and incident bladder cancer risk. There was no clear evidence for effect modification according to age group, sex, region, education, cigarette smoking status, or pack‐years. Results were also similar among more residentially stable participants and in two‐pollutant models. Overall, there was no clear evidence for associations of ambient PM2.5 and NO2 concentrations and incident bladder cancer risk. Further research in other large‐scale population studies is needed with detailed information on measured or modeled estimates of ambient air pollution concentrations and individual level risk factors.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| 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".