Lung cancer and occupation: A new zealand cancer registry‐based case–control study
Bibliographic record
Abstract
BACKGROUND: There are many proven and suspected occupational causes of lung cancer, which will become relatively more important over time, as smoking prevalence decreases. METHODS: We interviewed 457 cases aged 20-75 years notified to the New Zealand Cancer Registry during 2007-2008, and 792 population controls. We collected information on demographic details, potential confounders, and employment history. Associations were estimated using logistic regression adjusted for gender, age, ethnicity, smoking, and socio-economic status. RESULTS: Among occupations of a priori interest, elevated odds ratios (ORs) were observed for sawmill, wood panel and related wood-processing plant operators (OR 4.63; 95% CI 1.05-20.29), butchers (OR 8.77, 95% CI 1.06-72.55), rubber and plastics products machine operators (4.27; 1.16-15.66), heavy truck drivers (2.24; 1.19-4.21) and workers in petroleum, coal, chemical and associated product manufacturing (1.80; 1.11-2.90); non-significantly elevated risks were also observed for loggers (4.67; 0.81-27.03), welders and flame-cutters (2.50; 0.86-7.25), pressers (5.74; 0.96-34.42), and electric and electronic equipment assemblers (3.61; 0.96-13.57). Several occupations and industries not of a priori interest also showed increased risks, including nursing associate professionals (5.45; 2.29-12.99), enrolled nurses (7.95; 3.10-20.42), care givers (3.47; 1.40-8.59), plant and machine operators and assemblers (1.61; 1.20-2.16), stationary machine operators and assemblers (1.67; 1.22-2.28), food and related products processing machine operators (1.98; 1.23-3.19), laborers and related elementary service workers (1.45; 1.05-2.00), manufacturing (1.34; 1.02-1.77), car retailing (3.08; 1.36-6.94), and road freight transport (3.02; 1.45-6.27). CONCLUSIONS: Certain occupations and industries have increased lung cancer risks in New Zealand, including wood workers, metal workers, meat workers, textile workers and drivers. Am. J. Ind. Med. 54:89-101, 2011. © 2010 Wiley-Liss, Inc.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".