Association between Occupational Exposure to Textile Fibre Dusts and Lung Cancer in a Population-Based Case-Control Study in Montreal: A Preliminary Analysis Comparing Results from Three Analytical Methods
Bibliographic record
Abstract
Objectives: To compare results estimated using two causal inference methods, inverse probability of exposure weighting (IPEW) and G-computation, to that estimated using the conventional multivariable logistic regression, on the association between occupational exposure to textile fibre dusts and lung cancer risk.Methods: A population-based case-control study on lung cancer was conducted from 1996 to 2001 in Montreal, Canada. Cases were individuals diagnosed with incident lung cancer and population controls were randomly selected from electoral lists and frequency-matched to cases by age, sex and electoral district. Questionnaires on lifetime occupational history, smoking and demographic characteristics were collected during in-person interview. Experts reviewed subjects’ work history and assessed exposure to 294 agents, including textile fibre dusts. Odds ratios (OR) and their 95% confidence intervals (CIs) for the association between ever exposure to textile fibre dusts and lung cancer risk were estimated using three different methods: 1) IPEW, 2) G-computation, and 3) conventional multivariable regression.Results: The two causal inference methods produced higher point estimates (ORIPEW=1.17, 95% CI=0.86-1.57; ORG-computation=1.11, 95% CI=0.80-1.49) compared to that estimated using the conventional multivariable logistic regression (OR=0.87, 95% CI=0.68-1.11). However, all three sets of OR results were close to the null value.Conclusion: The different methods provided rather similar results, albeit not identical. They are compatible with a null association between occupational exposure to textile fibre dusts and lung cancer.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".