A hybrid expert approach for retrospective assessment of occupational exposures in a population-based case-control study of cancer
Bibliographic record
Abstract
BACKGROUND: While the expert-based occupational exposure assessment approach has been considered the reference method for retrospective population-based studies, its implementation in large study samples has become prohibitive. To facilitate its application and improve upon it we developed, in the context of a Montreal population-based study of prostate cancer (PROtEuS), a hybrid approach combining job-exposure profiles (JEPs) summarizing expert evaluations from previous studies and expert review. We aim to describe the hybrid expert method and its impacts on the exposures assigned in PROtEuS compared to those from a previous study coded using the traditional expert method. METHODS: Applying the hybrid approach, experts evaluated semi-quantitative levels of confidence, concentration and frequency of exposure to 313 agents for 16,065 jobs held by 4005 subjects in PROtEuS. These assessments were compared to those from a different set of jobs coded in an earlier study of lung cancer, conducted on the same study base, for 90 blue-collar occupations and 203 agents. Endpoints evaluated included differences in the number of exposures and in the distribution of ratings across jobs, and the within-occupation variability in exposure. RESULTS: Compared to jobs from the lung cancer study, jobs in PROtEuS had on average 0.3 more exposures. PROtEuS exposures were more often assigned definite confidence ratings, but concentration and frequency levels tended to be lower. The within-occupation variability in ratings assigned to jobs were lower in PROtEuS jobs for all metrics. This was particularly evident for concentration, although considerable variability remained with over 40% of occupation/agent cells in PROtEuS exposed at different levels. The hybrid approach reduced coding time by half, compared to the traditional expert assessment. CONCLUSIONS: The new hybrid expert approach improved on efficiency and transparency, and resulted in greater confidence in assessments, compared to the traditional expert method applied in an earlier study involving a similar set of jobs. Assigned ratings were more homogeneous with the hybrid approach, possibly reflecting clearer guidelines for coding, greater coherence between experts and/or reliance on summaries of past assessments. Nevertheless, significant within-occupation variability remained with the hybrid approach, suggesting that experts took into account job-specific factors in their assessments.
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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 | 0.042 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".