Lung cancer incidence in Canadian petroleum workers
Bibliographic record
Abstract
OBJECTIVES: This study's purpose was to conduct a more in-depth analysis of the potential association between lung cancer, occupational exposures and smoking using data on cohort members from a Canadian petroleum company and refined statistical analyses. METHODS: Information on various exposures including asbestos and petroleum coke dust, as well as job type and operating segment were collected via manual and computerised company records. We performed life-table analyses, Poisson regression and restricted cubic splines to model exposure-response patterns while controlling for smoking status and age. Model diagnostics included the assessment of dispersion and offset parameters. RESULTS: These analyses show that lung cancer risk is strongly related to age and smoking, and to a lesser extent to province of last residence. When controlling for these covariates, there is suggestive evidence that maintenance work may also be related to lung cancer risk. Some analyses also indicate that asbestos exposure may be associated with lung cancer risk, although a clear exposure-response trend is not seen. Other exposures, including petroleum coke dust, were not strongly related to lung cancer risk, particularly when expressed as a continuous measure. CONCLUSIONS: These data suggest that maintenance work may be associated with lung cancer incidence, although exposures to the single agents studied did not emerge as strong predictors of lung cancer incidence. Maintenance work may be a surrogate for general exposures to several agents (eg, polycyclic aromatic hydrocarbons, metals, welding fumes, radiation, etc), although these results may be affected by residual confounding due to smoking or other socio-demographic factors.
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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 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".