Invited Commentary: Epidemiologic Studies of the Impact of Air Pollution on Lung Cancer
Bibliographic record
Abstract
In this issue of the Journal, Villeneuve et al. (Am J Epidemiol. 2014;179(4):443-451) present epidemiologic evidence supporting the literature on the adverse effects of air pollution on risk of lung cancer. They found that ambient exposure to volatile organic compounds, especially when measured at longer time scales, was associated with increased odds of lung cancer in citizens of Toronto, Ontario, Canada, between 1997 and 2002. Specifically, in fully adjusted models, they observed that an interquartile-range increase in benzene concentration was associated with an odds ratio of 1.51 (95% confidence interval: 1.13, 2.01) using exposure at the time of interview. The odds ratio increased to 1.84 (95% confidence interval: 1.26, 2.68) when time-weighted exposure at all previous addresses was considered. They obtained similar results for exposure to nitrogen dioxide. These findings add weight to the substantial (and rapidly growing) body of literature on the relation of air pollution with lung cancer risk, as well as illustrate important aspects of the effects of different exposure assessment choices and potential sources of key interest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.037 | 0.028 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".