From Expert-based to Quantitative Retrospective Exposure Assessment at a Söderberg Aluminum Smelter
Bibliographic record
Abstract
OBJECTIVES: Expert judgement of exposure levels is often only poorly or moderately correlated with directly measured levels. For a follow-up of a historical cohort study at a Söderberg aluminum smelter we updated an expert-based semiquantitative job exposure matrix of coal tar pitch volatiles (CTPV) to quantitative estimates of CTPV and benzo(a)pyrene (BaP). METHODS: Mixed effects models to predict exposure for potroom operation and maintenance jobs were constructed from personal CTPV and BaP measurements. Mean exposures of jobs in non-potroom locations were directly calculated when measurements were available. Exposure estimates for jobs/time periods with no measurements were based on proportion of time spent in exposed areas compared to jobs where exposure was modeled or measured. For pre-1977, the original expert exposure assignments were calibrated using the updated 1977 estimates. RESULTS: The rate of change in exposure levels varied by time period and was accounted for in mixed models with a linear spline time trend. Other variables significant in the models were job, potroom group and season as fixed effects, and worker as a random effect. The models for potroom operations explained 45 and 27% of the variability in the CTPV and BaP measurements, respectively. The models for maintenance jobs explained 40 and 19% of the variability in the CTPV and BaP measurements, respectively. For 1977-2000 model estimates, direct calculation of means and extrapolation from modeled/measured exposures accounted for 57, 6 and 37% of the exposed person-years, respectively. CONCLUSIONS: The above methodology maximized the use of exposure measurements and largely replaced the original expert-based estimates. Finer discrimination between exposure levels was possible with the updated exposure assessment. The new estimates are expected to reduce exposure misclassification and help better assess the exposure-response relationships.
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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 | 0.024 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".