Identification of occupational cancer risk in British Columbia: A population‐based case–control study of 2,998 lung cancers by histopathological subtype
Bibliographic record
Abstract
BACKGROUND: Few studies have investigated occupational lung cancer risk in relation to specific histopathological subtypes. METHODS: A case-control study was conducted to evaluate the relationship between lung cancer and occupation/industry of employment by histopathological subtype. A total of 2,998 male cases and 10,223 cancer controls, diagnosed between 1983 and 1990, were identified through the British Columbia Cancer Registry. Matched on age and year of diagnosis, conditional logistic regression analyses were performed for two different estimates of exposure with adjustment for potentially important confounding variables, including tobacco smoking, alcohol consumption, marital status, educational attainment, and questionnaire respondent. RESULTS: For all lung cancers, an excess risk was observed for workers in the primary metal (OR = 1.31, 95% CI, 1.01-1.71), mining (OR = 1.53, 95% CI, 1.20-1.96), machining (OR = 1.33, 95% CI, 1.09-1.63), transport (OR = 1.50, 95% CI, 1.08-2.07), utility (OR = 1.60, 95% CI, 1.22-2.09), and protective services (OR = 1.27, 95% CI, 1.05-1.55) industries. Associations with histopathological subtypes included an increased risk of squamous cell carcinoma in construction trades (OR = 1.25, 95% CI, 1.06-1.48), adenocarcinoma for professional workers in medicine and health (OR = 1.73, 95% CI, 1.18-2.53), small cell carcinoma in railway (OR = 1.62, 95% CI, 1.06-2.49), and truck transport industries (OR = 1.51, 95% CI, 1.00-2.28), and large cell carcinoma for employment in the primary metal industry (OR = 2.35, 95% CI, 1.11-4.96). CONCLUSIONS: Our results point to excess lung cancer risk for occupations involving exposure to metals, polyaromatic hydrocarbons and asbestos, as well as several new histopathologic-specific associations that merit further investigation.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".