Occupational and environmental exposure to polychlorinated biphenyls and risk of non-Hodgkin lymphoma: a systematic review and meta-analysis of epidemiology studies
Bibliographic record
Abstract
We carryied out a meta-analysis of studies on exposure to polychlorinated biphenyls (PCBs) and risk of non-Hodgkin lymphoma (NHL). Through a systematic search of the literature, we identified relative risks (RRs) for PCB exposure and NHL risk in 30 populations (10 occupational exposure, seven high environmental exposure, 13 without special exposure). We performed random effects meta-analyses for exposure to all PCBs, specific PCB congeners and risk of all NHL and NHL subtypes. The meta-RR for studies of occupational exposure, high environmental exposure, and no special exposure were 0.94 [95% confidence interval (CI): 0.84-1.03], 1.05 (95% CI: 0.94-1.16), and 1.03 (95% CI: 0.72-1.34), respectively, and the cumulative meta-RR was 0.96 (95% CI: 0.85-1.07). No positive associations were found for exposure to specific congeners, nor for NHL subtypes. The meta-RR for an increase of 100 ppb serum or fat PCB level was 1.02 (95% CI: 1.00-1.04). There was weak indication of publication bias. Our meta-analysis found no association between PCB exposure and NHL risk, in particular in studies of occupational exposures. We detected a weak dose-response relation; the possibility of residual confounding and other sources of bias cannot be ruled out. PCBs are not likely to cause NHL in humans.
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 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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.037 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".