A Case-Control Study of Long-Term Exposure to Ambient Volatile Organic Compounds and Lung Cancer in Toronto, Ontario, Canada
Bibliographic record
Abstract
Few studies have investigated associations between nonoccupational exposure to ambient volatile organic compounds and lung cancer. We conducted a case-control study of 445 incident lung cancers and 948 controls (523 hospital, 425 general population) in Toronto, Ontario, Canada, between 1997 and 2002. Participants provided information on several risk factors, including tobacco use, secondhand exposure to cigarette smoke, obesity, and family history of cancer. Exposure to benzene, hydrocarbons, and nitrogen dioxide was estimated using land-use regression models. Exposures were linked to residential addresses to estimate exposure at the time of interview, 10 years before interview, and across past residences (time-weighted average). Logistic regression was used to estimate adjusted odds ratios. Analyses involving the population-based controls found that an interquartile-range increase in the time-weighted average benzene concentration (0.15 µg/m(3)) across previous residences was associated with lung cancer (odds ratio = 1.84, 95% confidence interval: 1.26, 2.68). Similarly, an interquartile-range increase in the time-weighted average nitrogen dioxide concentration (4.8 ppb) yielded an odds ratio of 1.59 (95% confidence interval: 1.19, 2.12). Our study suggests that long-term exposure to ambient volatile organic compounds and nitrogen dioxide at relatively low concentrations is associated with lung cancer. Further work is needed to evaluate joint relationships between these pollutants, smoking, and lung cancer.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".