Occupational exposure to nickel or hexavalent chromium and the risk of lung cancer (SYNERGY)
Bibliographic record
Abstract
Abstract Background Hexavalent chromium (Cr(VI)) and nickel (Ni) are known occupational carcinogens, with frequent exposures, for example, in welding-related activities. However, limited evidence exists on the exposure-response relationship of Cr(VI) or Ni with lung-cancer risk. We estimated lung-cancer risks based on quantitative indices of occupational exposure to each metal, and their interaction with smoking habits. Methods Fourteen case-control studies from Europe and Canada (16,901 cases, 20,965 control subjects) were pooled. A measurement-based job-exposure matrix was used to estimate year- and region-specific exposure levels for Cr(VI) and Ni, which were linked to the study subjects’ occupational histories. Odds ratios (OR) and 95% confidence intervals (CI) were calculated, adjusting for study, age group, smoking, and exposure to other occupational lung carcinogens. Results The OR for the highest quartile (>99.5 μg/m3-years) of cumulative exposure to Cr(VI) in men was 1.32 (95% CI 1.19-1.47) and for nickel (highest quartile >78.1 μg/m3-years) OR = 1.29 (95% CI 1.15-1.45). Corresponding effect estimates in women were slightly lower. The joint effects of Cr(VI) and nickel with smoking were generally greater than additive. Conclusions We observed positive exposure-response relationships in the low-dose range of Cr(VI) and Ni exposures, based on a large study population including detailed information on smoking habits and a measurement-based job-exposure matrix. Although, we cannot rule out a combined classical measurement and Berkson error structure that may have caused bias in our risk estimates, our results provide empirical evidence to further specify preventive measures at the workplace in order to protect workers from hazardous substances. Key messages • Relatively low cumulative levels of occupational exposure to Cr(VI) and Ni were associated with increased ORs for lung cancer, particularly in men. • Important empirical insights for the prevention of cancer at the workplace can be drawn from the results.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".