Meta-Analysis of Mortality and Cancer Incidence among Workers in the Synthetic Rubber-Producing Industry
Bibliographic record
Abstract
Production of synthetic rubber involves exposure to several potentially harmful chemicals. The authors carried out a systematic review and meta-analysis of cohort studies of workers in the rubber-producing industry. Data were obtained from computerized literature searches of several databases from their inception through December 2003. The reference lists of identified articles were inspected for further relevant articles. The authors conducted random-effects meta-analyses of log standardized mortality ratios (SMRs)/standardized incidence ratios. Heterogeneity between study results was explored through subgroup analyses and meta-regression on cohort demographic factors and study quality indicators. The authors identified 36 published articles reporting information on 31 different cohort groups. The meta-SMR was 0.86 (95% confidence interval (CI): 0.82, 0.91) for all-cause mortality (28 cohorts) and 0.94 (95% CI: 0.89, 1.01) for all malignant neoplasms (27 cohorts). Heterogeneity was observed for these endpoints and for the majority of disease-specific outcomes. Statistically significant excesses were observed for diabetes (meta-SMR=1.36, 95% CI: 1.17, 1.59) (five cohorts) and leukemia (meta-SMR=1.21, 95% CI: 1.03, 1.43) (16 cohorts), the latter particularly for persons working exclusively in nontire manufacturing (meta-SMR=1.70, 95% CI: 1.14, 2.54) (four cohorts). Excesses highlighted in previous narrative reviews were not substantiated. Interpretation of these results is complicated by substantial unexplainable heterogeneity; small excesses in specific mortality outcomes may have been disguised by the healthy worker effect.
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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 | 0.019 | 0.035 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.064 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".