A comparison of exposure assessment approaches: lung cancer and occupational asbestos exposure in a population-based case–control study
Bibliographic record
Abstract
OBJECTIVES: In attempts to overcome the limitations of self-reported data in occupational health research, job-exposure matrices, which assign exposure by occupation, have emerged as an objective approach for assessing occupational exposures. On the basis of a lung cancer case-control study conducted in the Greater Toronto Area, 1997-2002, assessment of occupational exposure to asbestos was compared using self-reports and a general population job-exposure matrix (DOM-JEM). METHODS: Cases and frequency matched controls provided life-time job histories and self-reported exposures to potential lung carcinogens including asbestos through a detailed questionnaire. Exposure to asbestos was also assigned to each job by linking occupational histories with DOM-JEM. Agreement in classification of exposed and unexposed jobs according to self-reports and DOM-JEM was evaluated using Cohen's κ. Risks for lung cancer were estimated using unconditional logistic regression for each exposure assessment approach. RESULTS: The prevalence of occupational asbestos exposure was greater when based on DOM-JEM than when based on self-reports. Agreement in classifying exposure to jobs between the two assessment approaches was poor. The risk of lung cancer was not elevated among workers who self-reported asbestos exposure, whereas workers considered exposed on the basis of DOM-JEM were almost twice as likely as unexposed workers to be diagnosed with lung cancer (OR 1.9, 95% CI 1.3 to 2.7). CONCLUSIONS: It is generally assumed by epidemiologists that self-reported exposure assessments result in inflated risk estimates. In this study, self-reports found no association with a well-established risk factor, whereas a high-quality job-exposure matrix revealed relative risk estimates that are more consistent with previous findings.
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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".