Multiple pesticide exposures and the risk of multiple myeloma in Canadian men
Bibliographic record
Abstract
Multiple myeloma (MM) has been linked to certain agricultural exposures, including pesticides. This analysis aimed to investigate the association between lifetime use of multiple pesticides and MM risk using two exposure metrics: number of pesticides used and days per year of pesticide use. A frequency-matched, population-based case-control study was conducted among men in six Canadian provinces between 1991 and 1994. Data from 342 MM cases and 1,357 controls were analyzed using logistic regression to calculate odds ratios (OR) and 95% confidence intervals. Pesticides were grouped by type, chemical class and carcinogenic potential, using a composite carcinogenic probability score. Selected individual pesticides were also examined. Regression models were adjusted for age, province of residence, use of proxy respondents, smoking and selected medical history variables. The overall pattern of results was complex. Positive trends in risk were observed for fungicides (ptrend=0.04) and pesticides classified as probably carcinogenic or higher (ptrend=0.03). Excess risks of MM were observed among men who reported using at least one carbamate pesticide (OR=1.94, 1.16-3.25), one phenoxy herbicide (OR=1.56, 1.09-2.25) and ≥3 organochlorines (OR=2.21, 1.05-4.66). Significantly higher odds of MM were seen for exposure to carbaryl (OR=2.71, 1.47-5.00) and captan (OR=2.96, 1.40-6.24). Use of mecoprop for >2 days per year was also significantly associated with MM (OR=2.15, 1.03-4.48). Focusing on multiple pesticide exposures is important because this more accurately reflects how exposures occur in occupational settings. Significant associations observed for certain chemical classes and individual pesticides suggest that these may be MM risk factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".