Exposure to Pesticides and Non-Hodgkin Lymphoma in Canadian Women
Bibliographic record
Abstract
ISEE-103 Objective: Exposure to pesticides is recognized as an important environmental factor associated with increased risk of cancer. The study examines the association between exposure to pesticides and the risk of non-Hodgkin lymphoma (NHL) in Canadian women. Material and Methods: Mailed questionnaires were completed by 789 incidents, histologically confirmed cases of NHL, and 2492 population controls between 1994 and 1997 in 8 Canadian provinces. Measurement included information on socioeconomic status, lifestyle habits, diet, occupation, or nonoccupational exposure to pesticides and years of exposure. Odds ratios (ORs) and 95% confidence intervals (CIs) were derived through unconditional logistic regression. Results: Exposure to pesticides had an increased risk of NHL. Compared with no exposure to pesticides, the OR was 1.5 (95% CI, 1.1–2.0). ORs increased with increasing exposure in years to pesticides (OR, 1.2 for 1–3 years exposure and 1.5 for >3 years). It was notable that 65% Canadian women exposed to pesticides at home and 30% in both at home and at work. Only 5% women exposed to pesticides at work. Conclusions: Case-control and cohort studies have given particular attention to agricultural pesticide use and risk of NHL. Most studies were focused on men. A number of studies reported that occupational exposure to pesticides increased the risk of NHL. We found that nonoccupational exposure to pesticides may play a major role in the etiology of NHL in Canadian women. Our findings add to the evidence that exposure to pesticides increased the risk of NHL.
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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 | 0.004 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".