Assessing the Global Impact of Ambient Air Pollution on Cancer Incidence and Mortality: A Comprehensive Meta-Analysis
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
PURPOSE This study aims to examine the association between exposure to major ambient air pollutants and the incidence and mortality of lung cancer and some nonlung cancers. METHODS This meta-analysis used PubMed and EMBASE databases to access published studies that met the eligibility criteria. Primary analysis investigated the association between exposure to air pollutants and cancer incidence and mortality. Study quality was assessed using the Newcastle Ottawa Scale. Meta-analysis was conducted using R software. RESULTS The meta-analysis included 61 studies, of which 53 were cohort studies and eight were case-control studies. Particulate matter 2.5 mm or less in diameter (PM 2.5 ) was the exposure pollutant in half (55.5%), and lung cancer was the most frequently studied cancer in 59% of the studies. A pooled analysis of exposure reported in cohort and case-control studies and cancer incidence demonstrated a significant relationship (relative risk [RR], 1.04 [95% CI, 1.02 to 1.05]; I 2 , 88.93%; P < .05). A significant association was observed between exposure to pollutants such as PM 2.5 (RR, 1.08 [95% CI, 1.04 to 1.12]; I 2 , 68.52%) and nitrogen dioxide (NO 2 ) (RR, 1.03 [95% CI, 1.01 to 1.05]; I 2 , 73.52%) and lung cancer incidence. The relationship between exposure to the air pollutants and cancer mortality demonstrated a significant relationship (RR, 1.08 [95% CI, 1.07 to 1.10]; I 2 , 94.77%; P < .001). Among the four pollutants, PM 2.5 (RR, 1.15 [95% CI, 1.08 to 1.22]; I 2 , 95.33%) and NO 2 (RR, 1.05 [95% CI, 1.02 to 1.08]; I 2 , 89.98%) were associated with lung cancer mortality. CONCLUSION The study confirms the association between air pollution exposure and lung cancer incidence and mortality. The meta-analysis results could contribute to community cancer prevention and diagnosis and help inform stakeholders and policymakers in decision making.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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