The effect of occupational exposure to petrol on pulmonary function parameters: a review and meta-analysis
Bibliographic record
Abstract
Introduction Exposure to petrol and gasoline can have harmful effects on the lungs. This review aimed to summarize the reported effects of this exposure on pulmonary function parameters. Methods Relevant studies were identified by a comprehensive search in PubMed, Scopus, Science Direct and Google Scholar databases. Irrelevant studies were excluded. Quality assessment was performed using the Newcastle-Ottawa score (NOS). The standard mean difference of pulmonary parameters between exposed and unexposed petrol station attendants was pooled using random effects. Meta-regression was used to investigate factors probably related to heterogeneity. Studies affecting the total estimates were assessed during sensitivity analysis. The Egger test was performed to investigate any evidence of publication bias. Results Eventually, 26 studies entered the meta-analysis, and the pooled standard difference [95% confidence interval (CI)] of forced vital capacity (FVC), forced expiratory volume in the first second (FEV1), FEV1/FVC, vital capacity (VC), peak expiratory flow (PEF), forced expiratory flow (FEF25-75) and maximum voluntary ventilation (MVV) in the exposed minus unexposed groups was -1.08 L (95% CI: -1.38, -0.78), -0.92 L (-1.15, -0.69), -0.65 (-1.01, -0.30), -0.51 L (-0.96, -0.06), -0.96 L/s (-1.21, -0.69), -0.78 L/s (1.14, -0.42) and -0.58 L/min (-0.90, -0.27), respectively, and showed a decrease in all pulmonary parameters in the exposed group. Conclusion Occupational exposure to petrol fumes is a risk factor for lung function and there is a reverse relation between lung function and the duration of exposure.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".