Exposures to multiple pesticides and the risk of Hodgkin lymphoma in Canadian men
Bibliographic record
Abstract
PURPOSE: To determine the risk of Hodgkin lymphoma (HL) associated with exposures to multiple pesticides grouped by various classes, including carcinogenic classifications. METHODS: Data collected in the Cross-Canada Study of Pesticides and Health, a population-based incident case-control study in six provinces conducted between 1991 and 1994, were analyzed using unconditional logistic regression. Cases (n = 316) were identified through provincial cancer registries and hospital records. Controls (n = 1,506) were frequency-matched to cases by age (± 2 years) within each province and were identified through provincial health records, telephone listings, or voter lists. The Cochran-Armitage test was used to check for trends within pesticide classes. RESULTS: Overall, there was an increase in the risk of HL among all subjects who reported use of five or more insecticides (OR 1.88, 95% CI 0.92-3.87) and among subjects younger than 40 who reported use of two acetylcholinesterase inhibitors (OR 3.16, 95% CI 1.02-9.29). There was an elevated odds ratio associated with reported use of three or more probably carcinogenic pesticides (OR 2.47, 95% CI 1.06-5.75), but no increase in risk for use of possibly carcinogenic pesticides. The risk of HL from reported use of fungicides or any pesticides was greater for cases diagnosed before age 40 than for cases diagnosed at or after age 40. When analyses excluded proxy respondents, OR estimates strengthened in some circumstances. CONCLUSIONS: This study found associations between HL and fungicides, insecticides, specifically acetylcholinesterase inhibitors, and pesticides previously identified as probable human carcinogens. These associations should be further evaluated, specifically in relation to age at diagnosis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".