Plasma Concentrations of Polychlorinated Biphenyls and the Risk of Breast Cancer: A Congener-specific Analysis
Bibliographic record
Abstract
Some reports indicate that exposure to specific polychlorinated biphenyl (PCB) congeners is related to breast cancer risk. The authors recruited participants in a case-control study from October 1994 to March 1997 to assess the relation between breast cancer risk and concentrations of 14 PCB congeners measured in plasma lipids by high-resolution gas chromatography. Participants were incident cases of breast cancer (n = 314) and controls (n = 523) from the Quebec City region (Canada). Compared with controls, cases had significantly higher concentrations of PCB 99 (p = 0.02), PCB 118 (p = 0.03), and PCB 156 (p = 0.006). Associations were found between breast cancer risk and either PCB 118 (odds ratio (OR) = 1.60, 95% confidence interval (CI): 1.01, 2.53; fourth vs. first quartile) or PCB 156 (OR = 1.80, 95% CI: 1.11, 2.94; fourth vs. first quartile) concentration. Breast cancer risk was also associated with a total concentration of the three mono-ortho-substituted congeners 105, 118, and 156 expressed as 2,3,7,8-tetrachlorodibenzo-p-dioxin toxic equivalents (OR = 2.02, 95% CI: 1.24, 3.28; fourth vs. first quartile). These results suggest that exposure to dioxin-like PCBs increases breast cancer risk. Alternatively, the results may be explained by differences between cases and controls regarding metabolic pathways involved in the biotransformation of both mono-ortho PCBs and estrogens.
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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 | 0.001 | 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 | 0.000 | 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".