Development of an asthma specific job exposure matrix and its application in the epidemiological study of genetics and environment in asthma (EGEA)
Bibliographic record
Abstract
OBJECTIVES: To develop a method suitable for estimating exposure risks in population studies of asthma from job titles and international codes, by combining a new job exposure matrix (JEM) with the expert judgement approach. The method was applied in the French epidemiological study of the genetics and environment in asthma (EGEA). METHODS: The JEM contains 22 exposure groups including 18 high risk groups based on known risk factors for occupational asthma, divided into high molecular weight agents, low molecular weight agents, and mixed environments. After applying the JEM to job codes, exposure estimates for each subject were re-evaluated by examining job title texts. Three high risk exposure estimates for asthma were compared: firstly, applying the JEM to original codes (from different coders in each study centre); secondly, applying the JEM to revised codes (from one experienced coder); and thirdly, after reviewing JEM exposure estimates in the light of job title texts. RESULTS: The study comprised 173 cases with asthma and 285 controls (age 18-65). Odds ratios (ORs) for asthma for high risk jobs were 1.0 (95% confidence interval (95% CI) 0.6 to 1.7), applying the JEM to original codes; 1.4 (95% CI 0.8 to 2.3), applying the JEM to revised codes; and 1.7 (95% CI 1.1 to 2.7), applying the JEM and subsequently re-evaluating exposure estimates from job title texts. Asthma ORs were 1.4 (95% CI 0.6 to 2.9) for high molecular weight agents, 2.3 (95% CI 1.2 to 4.4) for low molecular weight agents, and 2.1 (95% CI 0.9 to 5.2) for mixed environments. CONCLUSIONS: This asthma JEM, when enhanced by expert re-evaluation of exposure estimates from job title texts, may be a useful tool in general population studies of asthma. In this study, a 1.7-fold increase in prevalence odds of high risk exposures was found among asthmatic workers compared with controls, with risk magnitude varying for different classes of exposure.
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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 | 0.016 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".