Levels of circulating polychlorinated biphenyls and mammographic breast density.
Bibliographic record
Abstract
BACKGROUND: Polychlorinated biphenyls (PCBs) are ubiquitous chemicals found in the environment that accumulate in body fat and exhibit endocrine-disrupting properties. These compounds are therefore suspected of influencing breast cancer risk, but results from studies are inconsistent. To further clarify the role of PCBs in the etiology of breast cancer, the present study aimed to examine the relation of 24 PCB congener levels, which were considered individually and in combinations, with mammographic density, one of the risk factors most strongly associated with breast cancer. MATERIALS AND METHODS: Plasma PCB levels were measured by gas chromatography coupled to mass spectrometry in 106 post-menopausal women for whom mammographic density was measured using a computer-assisted method. RESULTS: Spearman correlation coefficients adjusted for potentially confounding factors (rs) show that while levels of total PCBs do not appear to be correlated with the percentage mammographic density (rs=-0.19, p=0.08), an increase in the plasma levels of congeners nos. 153, 183, 196 and combined Wolff group 3 PCBs is negatively correlated with the percentage mammographic density (rs=-0.24, p=0.03; rs=-0.30, p=0.004; rs=-0.22, p=0.04; and rs=-0.22, p=0.04 respectively). CONCLUSION: Our results suggest that an increase in the plasma levels of some PCB congeners, in particular cytochrome P450 1A1 inducers, is associated with lower mammographic density in post-menopausal women.
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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 | 0.000 | 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) | 0.002 | 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".