Organochlorines and risk of non‐Hodgkin lymphoma
Bibliographic record
Abstract
Organochlorine chemicals and polychlorinated biphenyls (PCBs) have been suspected as possible risk factors for non-Hodgkin lymphoma (NHL). We investigated PCBs and organochlorine pesticides and risk of NHL in a population-based case-control study in British Columbia, Canada. Congeners of PCBs (including dioxinlike congeners) and pesticides or pesticide metabolites were measured in plasma of 422 pretreatment cases and 460 control subjects. This is so far the largest study to examine organochlorines in plasma to date. Several dioxin-like PCB congeners were associated with increased risk of NHL, including dioxin-like PCB nos. 118 and 156 with odds ratios (OR) for the highest versus lowest quartile between 1.6 and 1.8. Several non-dioxin-like congeners also showed significant associations. The PCB congener with the strongest association was no. 180 with an OR for the highest versus the lowest quartile of 1.83 (95% confidence interval = 1.18-2.84). Six pesticide analytes also showed a significant association with NHL; beta-hexachlorocyclohexane, p,p'-DDE, hexachlorobenzene, mirex, oxychlordane and trans-nonachlor. The strongest association was found for oxychlordane, a metabolite of the pesticide chlordane (highest vs. lowest quartile OR = 2.68, 95% confidence interval = 1.69-4.24). Our results provide further evidence that organochlorines contribute to NHL risk.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".