Silicosis Incidence and Mortality after Occupational Exposure with Silica Dust: A Systematic Review and Dose-Response Meta-Analysis
Bibliographic record
Abstract
Background: We conducted a systematic review of all published epidemiological research related to the relationship between occupational silica exposure and the rates of silicosis incidence and mortality. Methods: We searched Scopus, PubMed, and Web of Sciences up to 11/07/2023, for original in any language. The search start date was not limited Observational studies, including cohort, case-control, and cross-sectional that have reported risk estimates for the association between silica exposure and silicosis mortality and incidence rates were considered. The methodological quality of the included articles was assessed using the Newcastle-Ottawa scale. Pooled estimates were calculated using the random effects model. Dose-response relations were explored through a two-stage random-effects model with "drmeta" command in Stata software version 14. Results: Nineteen observational studies were included in the present systematic review and meta-analysis. Based on the linear dose-response analysis, with each mg/m3 increase in daily occupational exposure to silica, the mortality risk of silicosis, the odds and risk of silicosis occurrence significantly increased by 10.19%, 360.02%, and 4.43 × 108%, respectively. Conclusion: This review revealed that there is a linear dose-response relationship between occupational exposure to silica and incidence and mortality from silicosis. Our findings could have practical applications for occupational and public health. Considering the direct relationship between occupational silica exposure and high incidence and mortality rates of silicosis, the level of silica dust should be decreased in different industries.
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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.038 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".