Adipose Organochlorine Concentrations and Risk of Breast Cancer Among Postmenopausal Danish Women
Bibliographic record
Abstract
OBJECTIVE: Exposure to environmental organochlorines has been examined as a potential risk factor for human breast cancer with mixed results. Our purpose was to examine associations between organochlorines and the development of breast cancer in a large prospective study using stored adipose tissue. METHODS: We conducted a nested case-control study of 409 postmenopausal women who developed breast cancer and 409 controls selected from the 29,875 women enrolled in the Danish Diet, Cancer, and Health cohort between 1993 and 1997. We measured concentrations of 14 pesticides and 18 polychlorinated biphenyls in adipose tissue, collected upon enrollment, and estimated relative risk (RR) of breast cancer using conditional logistic regression. RESULTS: The results showed no higher risk of breast cancer among women with higher levels of any pesticides or polychlorinated biphenyls; the RR associated with the upper quartile of 1,1-dichloro-2, 2-bis(p-chlorophenyl)ethylene concentration was 0.7 [95% confidence interval (95% CI), 0.5-1.2] contrasting the lower quartile, and for the sum of polychlorinated biphenyls the similar risk was 1.1 (95% CI, 0.7-1.7). We observed a pattern of substantially lower risk of estrogen receptor-negative breast cancer in association with higher levels of most of the pesticides and polychlorinated biphenyls; the RR for the higher quartile of 1,1-dichloro-2,2-bis(p-chlorophenyl)ethylene was 0.1 (95% CI, 0.0-0.5) and for the sum of polychlorinated biphenyls it was 0.3 (95% CI, 0.1-0.9). CONCLUSION: The results do not support that higher organochlorine body levels increase the risk of breast cancer in postmenopausal women. The interpretation of the inverse association for estrogen receptor-negative breast cancer is currently unclear.
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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 | 0.001 |
| 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".