Cancer Risks among Welders and Occasional Welders in a National Population-Based Cohort Study: Canadian Census Health and Environmental Cohort
Bibliographic record
Abstract
BACKGROUND: Welders are exposed to many known and suspected carcinogens. An excess lung cancer risk among welders is well established, but whether this is attributable to welding fumes is unclear. Excess risks of other cancers have been suggested, but not established. We investigated welding cancer risks in the population-based Canadian Census Health and Environmental Cohort. METHODS: Among 1.1 million male workers, 12,845 welders were identified using Standard Occupational Classification codes and followed through retrospective linkage of 1991 Canadian Long Form Census and Canadian Cancer Registry (1992-2010) records. Hazard ratios (HRs) were calculated using Cox proportional hazards models based on estimated risks of lung cancer, mesothelioma, and nasal, brain, stomach, kidney, and bladder cancers, and ocular melanoma. Lung cancer histological subtypes and risks by industry group and for occasional welders were examined. Some analyses restricted comparisons to blue-collar workers to minimize effects of potential confounders. RESULTS: Among welders, elevated risks were observed for lung cancer [HR: 1.16, 95% confidence interval (CI): 1.03-1.31], mesothelioma (HR: 1.78, 95% CI: 1.01-3.18), bladder cancer (HR: 1.40, 95% CI: 1.15-1.70), and kidney cancer (HR: 1.30, 95% CI: 1.01-1.67). When restricted to blue-collar workers, lung cancer and mesothelioma risks were attenuated, while bladder and kidney cancer risks increased. CONCLUSION: Excess risks of lung cancer and mesothelioma may be partly attributable to factors including smoking and asbestos. Welding-specific exposures may increase bladder and kidney cancer risks, and particular sources of exposure should be investigated. Studies that are able to disentangle welding effects from smoking and asbestos exposure are needed.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".