Correlation Between Nitrogen Dioxide as an Air Pollution Indicator and Breast Cancer: a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The aim of this systematic review was to study the relationship between exposure to nitrogen dioxide (NO2) in the ambient air and breast cancer incidence. MATERIALS AND METHODS: A systematic review was performed based on the MOOSE guideline for review of observational studies. We searched five online databases (PubMed, Science Direct, Google Scholar, EBSCO, and Scopus) from their conception to June 2014. A pooled estimate of the correlation between NO2 exposure and breast cancer incidence was calculated using Pearson's correlation coefficient. RESULTS: A total of 654 titles were retrieved in the initial search of the databases. Further refinement and screening of the retrieved studies produced a total of five studies from four countries. The studies included three ecological studies (aggregate level) and two individual based studies (one prospective cohort and the other one a case-control study). The ecological studies were pooled and the meta-analysis of correlation coefficient without z transformation showed a pooled estimate of r = 0.89 with 95% CI of 0.84 to 0.95. Using z transformation, the pooled r was 1.38 with 95%CI of 1.11 to 1.59. No significant heterogeneity between studies was observed. Following a sensitivity analysis and the removal of each study from pooled analysis we did not see any significant change in the pooled estimate. CONCLUSIONS: It was concluded that there is a tendency toward a weak association between exposure to NO2 in ambient air and breast cancer at the individual level and a significant association at the aggregate level.
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 | 0.017 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.027 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".