8287119 Impact of coded crosswalk methodologies on lung cancer risk estimates in Ontario, Canada workers exposed to diesel engine exhaust
Bibliographic record
Abstract
Objective Standardized occupation and industry classification systems of a job-exposure matrix (JEM) and study population may not match; crosswalks may be required for translating codes. Challenges in crosswalking between systems exist when there are multiple matches to a single occupation/industry code. Using crosswalks for exposure assessment and impacts on disease risk estimation are rarely discussed in the literature. This study investigates the effect of three crosswalk methods of the Diesel Exhaust in Canada Job-Exposure Matrix (DEC-JEM) on lung cancer risk in the Occupational Disease Surveillance System (ODSS). Methods Ontario workers in the ODSS (~2.2 million identified through workers’ compensation claims (1983-2020)) were followed for cancer diagnoses through linkage with the Ontario Cancer Registry. Three methods were used to crosswalk DEC-JEM to the codes in the ODSS: (1) unexposed matches excluded, average of one-to-multiple matches, (2) unexposed matches included, average of one-to-multiple matches, (3) unexposed matched included, weighted average of one-to-multiple matches. Cox-proportional hazards models of lung cancer were run with DEC-JEM to estimate hazard ratios and 95% confidence intervals, adjusted for age, birth year, and sex. Analyses were run at four exposure thresholds: 5%, 25%, 50%, 75%. Results DEC-JEM2 and DEC-JEM3 had similar case counts and exposure distributions for each exposure level and threshold (16-92% lower than DEC-JEM1, 25% exposure threshold). Increased lung cancer risk was observed using all methods (25% threshold). Differences in risk between methods were most noticeable for high exposed workers (HR range=1.12-1.73); little difference was observed amongst the low (HR range=1.28-1.35) and very high exposure (HR range=1.53-1.55) groups. DEC-JEM3 had the highest dose-response slope. Female workers had significant increased risk (all methods). Conclusion Crosswalk decisions can change a JEM’s exposure distribution, determine case exposure status, and influence the slope of disease risk results. This study’s crosswalk methods provide opportunities to save resources while improving exposure assessment.
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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 | 0.012 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".