Pesticide exposures and the risk of multiple myeloma in men: An analysis of the North American Pooled Project
Bibliographic record
Abstract
Multiple myeloma (MM) has been consistently linked with agricultural activities, including farming and pesticide exposures. Three case-control studies in the United States and Canada were pooled to create the North American Pooled Project (NAPP) to investigate associations between pesticide use and haematological cancer risk. This analysis used data from 547 MM cases and 2700 controls. Pesticide use was evaluated as follows: ever/never use; duration of use (years); and cumulative lifetime-days (LD) (days/year handled × years of use). Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using logistic regression adjusted for age, province/state of residence, use of proxy respondents and selected medical conditions. Increased MM risk was observed for ever use of carbaryl (OR = 2.02, 95% CI = 1.28-3.21), captan (OR = 1.98, 95% CI = 1.04-3.77) and DDT (OR = 1.44, 95% CI = 1.05-1.97). Using the Canadian subset of NAPP data, we observed a more than threefold increase in MM risk (OR = 3.18, 95% CI = 1.40-7.23) for ≤10 cumulative LD of carbaryl use. The association was attenuated but remained significant for >10 LD of carbaryl use (OR = 2.44; 95% CI = 1.05-5.64; ptrend = 0.01). For captan, ≤17.5 LD of exposure was also associated with a more than threefold increase in risk (OR = 3.52, 95% CI = 1.32-9.34), but this association was attenuated in the highest exposure category of >17.5 LD (OR = 2.29, 95% CI = 0.81-6.43; ptrend = 0.01). An increasing trend (ptrend = 0.04) was observed for LD of DDT use (LD > 22; OR = 1.92, 95% CI = 0.95-3.88). In this large North American study of MM and pesticide use, we observed significant increases in MM risk for use of carbaryl, captan and DDT.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".