Retrospective pesticide exposure assessment for studying multiple myeloma risk for farm work in British Columbia, Canada
Bibliographic record
Abstract
Multiple myeloma (MM) is one of the cancer types that research has shown to be in excess among farm workers. Pesticides have been highly suspected as a risk factor, and much of the research aimed at understanding MM etiology in farm workers has focused on pesticides. However, certain methodological challenges related to the retrospective assessment of pesticide exposure have contributed to weak and at times, inconsistent, epidemiologic evidence. The aims of this research were to collect and understand the usefulness of exposure data from the literature for future exposure assessment, to develop a method for estimating cumulative pesticide exposure levels in farm workers when combined with self-reported information, and to apply this method to evaluate the relationship between pesticide exposure and MM risk among a sample of farm workers in British Columbia (BC), Canada. The first study used a systematic approach to collect and evaluate the literature to identify studies that provided quantitative dermal pesticide exposure information on farm workers. The data were extracted and evaluated for usefulness for exposure assessment development. The second study involved the development of a pesticide exposure algorithm, and its application to farm data previously collected as part of an MM case-control study conducted in BC. The results of this algorithm were compared to the results of two other methods, one of which was a previously developed and evaluated algorithm from a well-known prospective cohort of pesticide applicators. The third study was an epidemiologic analysis to evaluate the relationship between pesticide exposure and MM among farm workers identified in the BC case-control study. Pesticide exposure was assessed using dichotomous, simple metrics of exposure as well as the two algorithms from the second study to estimate cumulative pesticide exposure. Overall, the research from this dissertation contributed new knowledge and provided additional evidence to support existing knowledge on the methodological issues surrounding the study of pesticide exposure in relation to MM among farm workers. Additionally, it provided a new pesticide exposure algorithm that can be further evaluated and improved upon regarding its ability to accurately assess exposure among farm workers using self-reported historical exposure information.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 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".