Blood Lead Level as Marker of Increased Risk of Ovarian Cancer in BRCA1 Carriers
Bibliographic record
Abstract
BRCA1 mutations substantially elevate the risks of breast and ovarian cancer. Various modifiers, including environmental factors, can influence cancer risk. Lead, a known carcinogen, has been associated with various cancers, but its impact on BRCA1 carriers remains unexplored. A cohort of 989 BRCA1 mutation carriers underwent genetic testing at the Pomeranian Medical University, Poland. Blood lead levels were measured using inductively coupled plasma mass spectrometry. Each subject was assigned to a category based on their tertile of blood lead. Cox regression analysis was used to assess cancer risk associations. Elevated blood lead levels (>13.6 μg/L) were associated with an increased risk of ovarian cancer (univariable: HR = 3.33; 95% CI: 1.23–9.00; p = 0.02; multivariable: HR = 2.10; 95% CI: 0.73–6.01; p = 0.17). No significant correlation was found with breast cancer risk. High blood lead levels are associated with increased risk of ovarian cancer in BRCA1 carriers, suggesting priority for preventive salpingo-oophorectomy. Potential risk reduction strategies include detoxification. Validation in diverse populations and exploration of detoxification methods for lowering lead levels are required.
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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 | 0.002 |
| 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) | 0.002 | 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".