Welding, longitudinal lung function decline and chronic respiratory symptoms: a systematic review of cohort studies
Bibliographic record
Abstract
While the acute respiratory risks of welding are well characterised, more chronic effects, including those on lung function, are less clear. We carried out a systematic review of published longitudinal studies of lung function decline in welders. Original cohort studies documenting two or more sequential measurements of lung function were reviewed. Meta-analysis was carried out on studies with suitable data on forced expiratory volume in 1 s (FEV1). Seven studies were included; their quality (measured on the Newcastle-Ottawa scale) was good, although exposure assessment was limited and the studies showed significant heterogeneity. Five had data suitable for meta-analysis; the pooled estimate of the difference in FEV1 decline between welders and nonwelders was -9.0 mL · year(-1) (95% CI -22.5-4.5; p=0.193). The pooled estimates of difference in annual FEV1 decline between welders and referents who smoked was -13.7 mL · year(-1) (95% CI -33.6-6.3; p=0.179). For welders and referents who did not smoke the estimated difference was -3.8 mL · year(-1) (95% CI -20.2-12.6; p=0.650). Symptom prevalence data were mainly narrative; smoking appeared to have the greatest effect on symptom evolution. Collectively, available longitudinal data on decline of lung function in welders and respiratory symptoms suggest a greater effect in those who smoke, supporting a focus on smoking cessation as well as control of fume exposure in this trade. Further prospective studies are required to confirm these findings.
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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.014 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".