Urinary Lead Exposure and Breast Cancer Risk in a Population-Based Case-Control Study
Bibliographic record
Abstract
BACKGROUND: Lead is a toxic nonessential metal with widespread exposure starting in utero. Lead has been reclassified in 2004 by the International Agency for Research on Cancer Working Group from a "possible" to a "probable" human carcinogen. Lead may be a facilitative or permissive carcinogen, which means that lead may permit or augment the genotoxic effects of other exposures. METHODS: This population-based study in Wisconsin gathered survey data and home-collected urine specimens from 246 women, ages 20 to 69 years, with incident invasive breast cancer identified from the Wisconsin state registry and 254 age-matched control subjects from population lists from September 2004 to February 2005. We measured urinary lead concentrations by inductively coupled plasma mass spectrometry, adjusted the values by specific gravity, and conducted interviews by telephone to obtain information on known and suspected breast cancer risk factors. RESULTS: Women in the highest quartile of specific gravity-adjusted lead level (>/=1.10 mug/L) had twice the breast cancer risk of those in the lowest quartile (<0.42 mug/L; odds ratio, 1.99; 95% confidence interval, 1.1-3.6) after adjustment for established risk factors. Excluding women who were currently taking nonsteroidal aromatase inhibitors (n = 52), we did not observe any increased breast cancer risk after adjustment for established risk factors. CONCLUSION: Our population-based case-control study suggests that lead exposure, as determined by specific gravity-adjusted urinary lead concentrations, is not associated with a significant increased risk for breast cancer.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".