Investigating the field effectiveness of respirators against metal particle exposure in various workplaces: a systematic review
Bibliographic record
Abstract
Abstract Welders are exposed to high levels of metal fumes, which could be resulting in various health impairments. Respirators became a practical protective option in workplaces, as they are lightweight and easy to use. This systematic review attempts to explore the field effectiveness of using respirators to reduce metal particle exposure in workplaces. We reviewed papers published from 1900 to April 2019 in five major bibliographic databases, including Embase, Web of Science, Medline, Scopus, and CINAHL, along with organizational websites to cover gray literature. In total, 983 references were identified from the databases, out of which, 520 duplicates were removed from the EndNote database. The remaining 463 references were screened for their title and abstract. Out of 463, 70 references went through the full-text screening. Finally, eight papers, including 19 workplace respirator studies, satisfied all the inclusion criteria and were reviewed in this report. The geometric means for metal levels in workers’ breathing zone with and without respirators were 9.4 and 1,777 µg/m3 for iron, 1.1 and 139 µg/m3 for lead, 2.1 and 242 µg/m3 for zinc, and 27 and 1,398 µg/m3 for manganese oxide, respectively. Most reviewed studies reported significant differences between measured metal particle levels among workers who worn respirators and who did not. In addition, results showed that N95 provided significantly less protection than elastomeric half facepieces, full-face respirators, and powered air-purifying respirators (p<0.001). More field studies are recommended to investigate Workplace Protection Factor (WPF) and fit factor (FF) of different respirators to understand the actual protection levels that they could be provided to control welding fume exposure among welders in various workplaces.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".