380 Multiple pesticide exposures and the risk of multiple myeloma in Canadian men
Bibliographic record
Abstract
<h3>Objectives</h3> Multiple myeloma (MM) has been linked to certain agricultural exposures, including pesticides, however the effects of exposure to multiple pesticides have not been explored. This analysis investigated the association between self-reported use of multiple pesticides and MM risk. Commonly used pesticide combinations and interactive effects were also assessed. <h3>Methods</h3> A frequency matched population-based case-control study was conducted among men in 6 Canadian provinces between 1991 and 1994. Data from 342 MM cases and 1506 controls were analysed using logistic regression to calculate odds ratios (OR) and 95% confidence intervals (CI). Pesticides were grouped by type, chemical class and carcinogenicity. Carcinogenic probability values were created using evaluations from the International Agency for Research on Cancer and U. S. Environmental Protection Agency. Regression models were adjusted for age, province of residence, use of proxy respondents, smoking, and selected medical history variables. Trends were examined using ordinal variables. Commonly used pesticide combinations were assessed for interaction on the additive scale using the interaction contrast ratio (ICR). <h3>Results</h3> Multiple pesticide use was not associated with monotonically increasing odds of MM, although positive trends were observed for “probably” carcinogenic pesticides (p<sub>trend</sub> = 0.01), insecticides (p<sub>trend</sub> = 0.07), and fungicides (p<sub>trend</sub> = 0.05). Higher odds of MM were observed among men who reported using at least one carbamate pesticide (OR = 1.99, 95% CI: 1.19–3.33), one phenoxyherbicide (OR = 1.60, 95% CI: 1.11–2.30), 3 or more “probably” carcinogenic pesticides (OR = 2.14, 95% CI: 1.01–4.52), and 3 or more organochlorines (OR = 2.26, 95% CI: 1.07–4.78). Investigating commonly used pesticide combinations, revealed increased odds among men who used both chlordane and mecoprop (OR = 2.18, 95% CI: 1.12–4.27; ICR = 0.63). <h3>Conclusions</h3> Focusing on multiple pesticides is important because this more accurately reflects how exposures occur in occupational settings. Although the overall pattern of results was complex, excess risks observed for certain pesticide types and chemical classes suggest these may be MM risk factors.
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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) | 0.000 | 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".