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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".