Quantitative risk assessment for lung cancer after exposure to bitumen fume
Bibliographic record
Abstract
An international cohort of asphalt workers was assembled to study cancer risk after bitumen exposure. This article describes the combination of the exposure assessment with the exposure-response for a quantitative risk assessment for lung cancer mortality within the Dutch component of the study. We identified a retrospective cohort of 3,709 workers with at least one season of employment. Semi-quantitative exposure to bitumen fume was estimated by a job-exposure matrix. Exposure-response relations were fitted by Poisson regression, and excess lifetime risks through age 75 were calculated by a life table method. Working lifetime cumulative exposure to bitumen fume was calculated under different scenarios, representing past and future exposures. For workers with exposures accumulated in the past, excess risks for lung cancer varied from 7.8 to 14.3%. Calculations for future exposures resulted in considerably lower excess risks ranging from 0.6 to 2.6%. The calculated excess risks for lung cancer mortality after working lifetime exposure to bitumen fume depend strongly on when exposure was experienced and to some extent on the exposure-response model chosen, while confounding by smoking cannot be ruled out. Nevertheless, the excess lifetime risk for lung cancer in this Dutch cohort of asphalt workers is above benchmark risks as applied by the Dutch Health Council. Current exposure levels have decreased this risk considerably, but further exposure control may be required.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".