Investigating the Association Between Traffic-related Air Pollution (PM2.5 and Benzene) and the Risk of Asthma: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background and aims: Asthma is a chronic disease that causes respiratory system inflammation. Recently, traffic-related air pollution (TRAP), especially particulate matter (PM2.5) and benzene, has been considered a factor that may increase the risk of asthma. This study investigated the association between TRAP (PM2.5 and benzene) and asthma risk. Methods: In this systematic review and meta-analysis, the relevant published data were collected by searching the Cochrane Library, Web of Science, ScienceDirect, Scopus, PubMed, and Google Scholar databases up to November 2022. The study quality was evaluated using the Newcastle-Ottawa Scale checklist. The data were analyzed using Stata software (version 14), and the significance level in this meta-analysis study was considered to be<0.05. Results: In the first search, 4,909 and 4,825 studies were extracted for PM2.5 and benzene, respectively. After evaluating and considering the search criteria, 25 and 4 studies remained for PM2.5 and benzene, respectively. For PM2.5, the odds ratio (OR) for developing asthma in the TRAP-exposed group compared to the unexposed group was 1.11 (95% confidence interval [CI]: 1.04-1.19, P=0.002). For benzene, the OR of developing asthma in the exposed group was 1.19 when compared to the unexposed group (95% CI: 1.10-1.29, P<0.001). Conclusion: Based on this review study, there was a positive association between TRAP exposure and the development of asthma. The results confirmed that PM2.5 and benzene increase the risk of asthma.
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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.043 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".