Insecticide use and risk of non‐Hodgkin lymphoma subtypes: A subset meta‐analysis of the North American Pooled Project
Bibliographic record
Abstract
Abstract Insecticide use has been linked to increased risk of non‐Hodgkin lymphoma (NHL), however, findings of epidemiologic studies have been inconsistent, particularly for NHL subtypes. We analyzed 1690 NHL cases and 5131 controls in the North American Pooled Project (NAPP) to investigate self‐reported insecticide use and risk of NHL overall and by subtypes: follicular lymphoma (FL), diffuse large B‐cell lymphoma (DLBCL) and small lymphocytic lymphoma (SLL). Odds ratios (OR) and 95% confidence intervals for each insecticide were estimated using logistic regression. Subtype‐specific associations were evaluated using ASSET (Association analysis for SubSETs). Increased risks of multiple NHL subtypes were observed for lindane (OR = 1.60, 1.20‐2.10: FL, DLCBL, SLL), chlordane (OR = 1.59, 1.17‐2.16: FL, SLL) and DDT (OR = 1.36, 1.06‐1.73: DLBCL, SLL). Positive trends were observed, within the subsets with identified associations, for increasing categories of exposure duration for lindane ( P trend = 1.7 × 10 −4 ), chlordane ( P trend = 1.0 × 10 −3 ) and DDT ( P trend = 4.2 × 10 −3 ), however, the exposure‐response relationship was nonlinear. Ever use of pyrethrum was associated with an increased risk of FL (OR = 3.65, 1.45‐9.15), and the relationship with duration of use appeared monotonic (OR for >10 years: OR = 5.38, 1.75‐16.53; P trend = 3.6 × 10 −3 ). Our analysis identified several novel associations between insecticide use and specific NHL subtypes, suggesting possible etiologic heterogeneity in the context of pesticide exposure.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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".