Prevalence of obstructive lung patterns and actual spirometric result at different workplaces in Ethiopia: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Background and Aims Burdens of occupational exposure like insoluble dust particles and chemicals in the respiratory tract leads to impaired clearance. This study aims to assess the prevalence of obstructive lung patterns and actual spirometric result in Ethiopia at different workplaces. Methods Five electronic databases such as: PubMed, HINARI, Science Direct, Google Scholar, and African Journals Online were searched in studies conducted from 2010 to 2021. In this study, we used STATA 14 software for data analysis and the quality of included studies were appraised using the New Castle Ottawa quality assessment tool. The pooled prevalence of obstructive lung patterns and actual spirometric results were estimated using effect size and standardized mean differences (SMD). Results A total representative of 3511 participants was included in this study. The pooled prevalence of obstructive lung patterns in occupational exposure at different workplaces was found 13.04% (95% CI: 7.96, 18.12, I2 = 89.2%). On the other hand, the pooled prevalence of obstructive lung patterns in controls was 4.10% (95% CI: 1.86, 6.34, I2 = 76.8%). SMD of spirometric results was significantly decreased in cases as compared to the controls. The SMD of FVC in a litter (L) at 95% of CI: −0.50 (−0.70, −0.30, I2 = 87.7%), SMD of FEV1 in (L) at 95% CI: −0.54 (−0.72, −0.36, I2 = 84.9%), SMD of FEF25%–50% in litter per second (L/s) at 95% of CI: −0.42 (−0.67, −0.17, I2 = 81.9%) and SMD of PEFR in L/s at 95% CI: −0.45 (−0.68, −0.21, I2 = 78.4%) were significantly decreased in cases as compared to the controls. Conclusion The pooled prevalence of obstructive lung pattern was higher in people who are working at different workplace that generating dusts and chemicals. The SMD of actual spirometric results were reduced in cases than controls. Therefore, to alleviate this problem appropriate preventive measure would be warranted for those people who are working in different dust and chemical generating environments.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.008 | 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 | 0.001 |
| 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".