S58 The exposure-response relationship between respirable crystalline silica and chronic silicosis: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Silicosis presents a significant global disease burden in mining and non-mining populations. Cumulative respirable crystalline silica (RCS) is thought to determine silicosis risk but no quantitative analysis has been performed and the reported literature is conflicting on the nature of this relationship. The UK government has recommended the evidence supporting the reduction in the RCS workplace exposure-limit (WEL) from 0.1 mg/m3 to 0.05 mg/m3 is assessed. This review aimed to quantitatively analyse the relationship between cumulative RCS exposure and chronic silicosis risk, and to assess the impact of intensity and industry on this relationship. Methods We searched Medline, Embase, Web of Science, a previous narrative review and US evidence summary report for eligible studies published before 24/02/23. Inclusion criteria included: silicosis risk stratified by cumulative RCS exposure categories and ≥20-year mean latency between first exposure and latest radiograph assessment. Cumulative risk for dose categories were calculated with a lifetable approach. Study fitted curves were plotted for comparison. Using the lifetables, a dose-response meta-analysis was performed. Risk of bias was assessed with a modified Newcastle-Ottawa Scale. Results From 782 studies, seven studies were selected and assessed as having a low risk of bias, which contributed nine cohorts (seven mining and two non-mining). Overall, 10,400 silicosis cases were reported among 66,013 workers. The rate of increase in cumulative silicosis risk with increasing cumulative RCS dose appeared greater in mining than non-mining industries (figure 1). A reduction from 2 mg/m3-years – equivalent to 20 years work at 0.1 mg/m3 average exposure – to 1 mg/m3-years resulted in a relative risk of 0.32 (95% CI 0.22–0.47) for miners and 0.76 (95% CI 0.73–0.79) for non-miners, however heterogeneity was high for both groups (I296.7% and 50.0%, respectively). Discussion Despite risk of bias from underestimated RCS exposure and high heterogeneity in our meta-analysis, our findings support recommendations to further reduce RCS WELs. Due to higher reported risks for mining cohorts, results additionally support more stringent WELs for mining industries to achieve equitable silicosis risk reduction. Further research investigating the underlying cause for risk disparity, and broadening evidence to encompass other industries is recommended.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.026 |
| Bibliometrics | 0.007 | 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) | 0.012 | 0.001 |
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".