Impact of the Specificity of the Exposure Metric on Exposure???Response Relationships
Bibliographic record
Abstract
BACKGROUND: Exposure misclassification may occur when nonspecific exposure indicators are used. Developing estimates of more specific measures may be difficult due to sampling limitations or a paucity of historical measurements and, thus, often requires substantial effort. We examine the impact on exposure-response relationships of moving from 2 measures of exposure mixtures (dust, chlorophenols) to more specific exposure indicators (wood dust, pentachlorophenol, tetrachlorophenol) in a retrospective cohort. METHODS: The study population consisted of 26,847 male sawmill workers (> or =1 year employment between 1950 and 1995) with linkage to national cancer registries. A subcohort (n = 11,273 employed more than 1 day between 1985 and 1995) was linked to hospital discharge records. We evaluated the shape (log-linear vs log-log models), goodness of fit, precision, and expected versus observed attenuation of the exposure-response relationships. RESULTS: The correlation between the cumulative exposure indices was moderately high (dust/wood dust, r = 0.68; total chlorophenol/pentachlorophenol, r = 0.88; total chlorophenol/tetrachlorophenol, r = 0.78). An increase in chronic obstructive pulmonary disease hospitalizations was found with wood dust but not with total dust. Stronger associations for non-Hodgkin lymphoma and kidney cancer incidence were observed with pentachlorophenol than with total chlorophenol; no association was observed with tetrachlorophenol. We observed greater attenuation than expected using total dust, but less than expected using total chlorophenol. CONCLUSIONS: The relationships between health outcomes were substantially attenuated when nonspecific exposure indicators were used. This study demonstrates the importance of developing exposure metrics as specific to the disease-causing agent as possible, particularly when the composition of mixed exposures varies by work areas.
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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 | 0.203 | 0.440 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".