Comparison of Assessment Methods for Pesticide Exposure in a Case-Control Interview Study
Bibliographic record
Abstract
In epidemiologic studies, much of the variation in disease risk estimates associated with occupational pesticide exposure may be due to variation in exposure classification. The authors compared five different methods of using interview information to assess occupational pesticide exposure in a US-Canada case-control study of neuroblastoma (1992-1994). For each method, exposure assignment was compared with that of a reference method, and neuroblastoma effect estimates were calculated. Compared with the reference method, which included a complete review of occupation, industry, job tasks, and exposure-specific activities, the use of occupation-industry groups alone or in combination with general job task information diluted the exposed group by including individuals who were unlikely to have been truly exposed. The effect estimates representing associations between each exposure method and neuroblastoma were different enough to influence the study's conclusions, especially when the exposure was rare (for maternal occupational pesticide exposure, the odds ratio was 0.7 using the reference exposure assessment method and 3.2 using the occupation-industry group exposure assessment method). Exposure-specific questions about work activities can help investigators distinguish truly exposed individuals from those who report exposure but are unlikely to have been exposed above background levels and from those who have not been exposed but are misclassified as exposed because of their employment in an occupation-industry group determined a priori to be exposed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.320 | 0.571 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".