Long-term Ambient Fine Particulate Matter Air Pollution and Lung Cancer in a Large Cohort of Never-Smokers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
RATIONALE: There is compelling evidence that acute and chronic exposure to ambient fine particulate matter (PM(2.5)) air pollution increases cardiopulmonary mortality. However, the role of PM(2.5) in the etiology of lung cancer is less clear, particularly at concentrations that prevail in developed countries and in never-smokers. OBJECTIVES: This study examined the association between mean long-term ambient PM(2.5) concentrations and lung cancer mortality among 188,699 lifelong never-smokers drawn from the nearly 1.2 million Cancer Prevention Study-II participants enrolled by the American Cancer Society in 1982 and followed prospectively through 2008. METHODS: Mean metropolitan statistical area PM(2.5) concentrations were determined for each participant based on central monitoring data. Cox proportional hazards regression models were used to estimate multivariate adjusted hazard ratios and 95% confidence intervals for lung cancer mortality in relation to PM(2.5). MEASUREMENTS AND MAIN RESULTS: A total of 1,100 lung cancer deaths were observed during the 26-year follow-up period. Each 10 μg/m(3) increase in PM(2.5) concentrations was associated with a 15-27% increase in lung cancer mortality. The association between PM(2.5) and lung cancer mortality was similar in men and women and across categories of attained age and educational attainment, but was stronger in those with a normal body mass index and a history of chronic lung disease at enrollment (P < 0.05). CONCLUSIONS: The present findings strengthen the evidence that ambient concentrations of PM(2.5) measured in recent decades are associated with small but measurable increases in lung cancer mortality.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it