Lung cancer and occupational exposures other than cotton dust and endotoxin among women textile workers in Shanghai, China
Bibliographic record
Abstract
OBJECTIVES: Numerous epidemiological studies of lung cancer among textile workers worldwide consistently indicate reduced risks related to cotton dust exposure, presumably due to endotoxin contamination. Our objective was to investigate associations with other exposures potentially related to lung cancer, including wool and synthetic fibre dusts, formaldehyde, silica, dyes and metals, that have only been studied to a limited extent in the textile industry. METHODS: We conducted a case-cohort study nested within a cohort of 267,400 women textile workers in Shanghai, China. We compared work assignments and exposure histories of 628 incident lung cancer cases, diagnosed during 1989-1998, with those of a reference subcohort of 3188 workers. We reconstructed exposures with a job-exposure matrix developed specifically for textile factories. Cox proportional hazards modelling was applied to estimate age/smoking-adjusted relative risks (hazard ratios) and risk gradients associated with job assignments and specific agents other than cotton dust and endotoxin. RESULTS: No associations were observed for lung cancer with wool, silk or synthetic fibre dusts, or with most other agents. However, increased risks, although statistically imprecise, were noted for ≥ 10 years' exposures to silica (adjusted HR 3.5, 95% CI 1.0 to 13) and ≥ 10 years' exposures to formaldehyde (adjusted HR 2.1, 95% CI 0.4 to 11). CONCLUSIONS: Exposures to silica and formaldehyde, although not widespread among the cohort, may have increased lung cancer risk. Silica is an established human lung carcinogen, whereas there is only weak prior evidence supporting an association with formaldehyde. Both exposures warrant consideration as potential lung carcinogens in textile manufacturing.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".