Bilateral Oophorectomy and the Risk of Breast Cancer in <i>BRCA1</i> Mutation Carriers: A Reappraisal
Bibliographic record
Abstract
BACKGROUND: The lack of consensus on whether bilateral oophorectomy impacts risk of developing breast cancer among BRCA1 mutation carriers might be attributed to various biases, specifically, cancer-induced testing bias due to inclusion of prevalent cases. We conducted two complementary matched case-control analyses to evaluate the association of oophorectomy and BRCA1 breast cancer. METHODS: A research questionnaire was administered every two years to collect information on exposures and disease. In the first analysis, we limited the study to prevalent breast cancer cases (diagnosed prior to study entry; n = 2,962) who were matched to controls on year of birth and country of residence (n = 4,358). In the second approach, we limited to 330 incident cases (diagnosed in the follow-up period) and 1,548 matched controls. Conditional logistic regression was used to estimate the adjusted odds ratios (OR) and 95% confidence intervals (CI) of invasive breast cancer. RESULTS: In the first approach, there was a significant inverse association between oophorectomy and the risk of developing breast cancer [OR = 0.43; 95% confidence interval (CI), 0.34-0.55; P < 00001]. In the second approach, there was no association between oophorectomy and risk (OR = 1.21; 95% CI, 0.87-1.70; P = 0.26). CONCLUSIONS: The inclusion of women with a personal history of breast cancer prior to ascertainment likely impacts upon the association of oophorectomy and BRCA1 breast cancer risk. IMPACT: Oophorectomy is unlikely a determinant of breast cancer risk in BRCA1 mutation carriers but should be offered at age 35 to reduce the risk of ovarian and fallopian tube 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".