Multiple Myeloma and Exposure to Pesticides: A Canadian Case-Control Study
Bibliographic record
Abstract
The objective of this study was to investigate the putative associations of specific pesticides with multiple myeloma. A matched, population-based, case-control study was conducted among men residing in six Canadian provinces (Quebec, Ontario, Manitoba, Saskatchewan, Alberta, and British Columbia). Data were collected on 342 multiple myelome cases and 1506 age and province of residence matched controls. Data were collected by mailed questionnaires to capture demographic characteristics, antecedent medical history, detailed lifetime occupational history, smoking history, family history of cancer, and exposure to broadly characterized pesticides at home, work, and practicing hobbies. Details of pesticide exposures were collected by telephone interview for those who reported 10 hours or more per year of exposure. Exposure to pesticides grouped into major chemical classes resulted in increased risk being detected only for carbamate insecticides [odds ratio (OR) and 95% confidence interval (CI) 1.90 (1.11, 3.27) adjusted for potential confounders]. An exposure to fungicide captan [2.35 (1.03, 5.35)] was positively associated with the incidence of multiple myeloma. While an exposure to carbaryl [1.89 (0.98, 3.67)] was associated with the incidence of multiple myelome with borderline significance. The authors further suggest that certain pesticide exposures may have a role in multiple myeloma etiology, and identify specific factors warranting investigation in other populations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".