Comparison of exposure estimates in the Finnish job-exposure matrix FINJEM with a JEM derived from expert assessments performed in Montreal
Bibliographic record
Abstract
CONTEXT: Retrospective exposure assessment in population-based case-control studies poses a major challenge due to the wide range of occupations and industries involved. The FINJEM is a generic job-exposure matrix (JEM) developed in Finland, which represents a potentially cost-effective exposure assessment tool. While FINJEM has been used in several studies outside Finland, little is known of its applicability in other countries. METHODS: We compared prevalence and intensity of exposure in FINJEM with a JEM developed from expert assessments of occupational histories obtained in a population-based case-control study in Montreal. Agreement for prevalence of exposure was measured by weighted κ coefficients between prevalence categories. Agreement for exposure intensity was measured by Spearman correlation coefficients between cells with non-null exposure. RESULTS: The comparison involved 27 chemicals, the time period 1945-1995 and included 4743 jobs initially assessed by the Montreal experts. 4293 combinations of agent, occupational title and period were available for comparison of prevalence. Agent-specific prevalence was consistently higher in the Montreal JEM (median difference 1.7%). Agent-specific κ values between prevalence categories varied from 0.89 (welding fumes) to 0.07 (flour dust). The comparison of exposure levels involved 14 agents and 198 cells with non-null exposure in both sources. Agent-specific Spearman correlation varied from 0.89 (flour dust) to -0.35 (benzo(a)pyrene). CONCLUSION: Our observations suggest that information concerning several agents (eg, metals, welding fumes) can be successfully transported from Finland to Canada and probably other countries. However, for other agents, there was considerable disagreement, and hence, transportability of FINJEM cannot be assumed by default.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".