Air Pollutants and Incidence of All-Cause, Lung, and Bladder Cancer in the Gazel Cohort (1989-2014)
Bibliographic record
Abstract
Background/AimWhile air pollutants – fine particulate matter (PM2.5), nitrogen dioxide (NO2), ozone (O3) and black carbon (BC) – are associated with mortality, their association with cancer incidence remains unclear. We aimed to analyze the relationships between these pollutants and the incidence of all-cause, lung and bladder cancer in the French general population-based cohort Gazel.MethodsLand use regression models with back-extrapolation were used to assess the long-term exposure to PM2.5, NO2, O3 and BC at home addresses of 19,530 participants, as the average exposure between enrolment and cancer incidence or censoring, whichever came first, with a 10-year lag to account for the time between initial exposure and the development of cancer. Follow-up was from 1989 to 2014. We used Cox models to derive hazard ratios (HR) for an interquartile range (IQR) increase of single pollutant exposure, adjusted for lifestyle and socioeconomic individual covariables at baseline including gender and occupational exposures, and with age as the underlying time scale.ResultsWe found significant associations between PM2.5 (IQR 7 µg/m3) and incident all-cause and lung cancer with respective HR of 1.15 (CI 1.10-1.21) and 2.08 (1.76-2.45); between NO2 (IQR 21 µg/m3) and all-cause and lung cancer with respective HR of 1.05 (1.01-1.10) and 1.32 (1.11-1.57); between BC (IQR 1 µg/m3) and all-cause and lung cancer with respective HR of 1.05 (1.01-1.09) and 1.43 (1.23-1.66). No significant association was found between O3 and incident cancers, nor between any pollutant and bladder cancer .ConclusionsPM2.5, NO2 and BC are associated with incidence of all-cause and specifically lung cancer in a general population-based cohort.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".