Pesticide exposure and lung function: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Epidemiological studies have reported associations between pesticide exposure and respiratory health effects, but the quantitative impact on lung function is unclear. To fill this gap, we undertook a systematic review of the available literature on the association between pesticide exposure and pulmonary function. AIMS: To examine all available literature regarding the relationship between occupational and environmental exposure to pesticides and lung function. METHODS: We searched MEDLINE, EMBASE and Web of Science databases to 1 October 2017 without any date or language restrictions using a combination of MeSH terms and free text for 'pesticide exposure' and 'lung function'. We included studies that met the criteria of our research protocol registered in PROSPERO, and we assessed their quality using a modified Newcastle-Ottawa scale. RESULTS: Of 2356 articles retrieved, 56 articles were included in the systematic review and pooled in meta-analyses for forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC), FVC and FEV1. There was tentative evidence that exposure to cholinesterase (ChE) inhibiting pesticides reduced FEV1/FVC and no evidence that paraquat exposure affected lung function in farmers. CONCLUSIONS: Respiratory surveillance should be enhanced in those exposed to ChE-inhibiting pesticides which reduced FEV1/FVC according to the meta-analysis. Our study is limited by heterogeneity between studies due to different types of exposure assessment to pesticides and potential confounders. Further studies with a more accurate exposure assessment are suggested.
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".