Contralateral Breast Cancer in <i>BRCA1</i> and <i>BRCA2</i> Mutation Carriers
Bibliographic record
Abstract
Purpose To estimate the risk of contralateral breast cancer in BRCA1 and BRCA2 carriers after diagnosis and to determine which factors are predictive of the risk of a second primary breast cancer. Patients and Methods Patients included 491 women with stage I or stage II breast cancer, for whom a BRCA1 or BRCA2 mutation had been identified in the family. Patients were followed from the initial diagnosis of cancer until contralateral mastectomy, contralateral breast cancer, death, or last follow-up. Results The actuarial risk of contralateral breast cancer was 29.5% at 10 years. Factors that were predictive of a reduced risk were the presence of a BRCA2 mutation (v BRCA1 mutation; hazard ratio [HR], 0.73; 95% CI, 0.47 to 1.15); age 50 years or older at first diagnosis (v ≤ 49 years; HR, 0.63; 95% CI, 0.36 to 1.10); use of tamoxifen (HR, 0.59; 95% CI, 0.35 to 1.01); and history of oophorectomy (HR, 0.44; 95% CI, 0.21 to 0.91). The effect of oophorectomy was particularly strong in women first diagnosed prior to age 49 years (HR, 0.24; 95% CI, 0.07 to 0.77). For women who did not have an oophorectomy or take tamoxifen, the 10-year risk of contralateral cancer was 43.4% for BRCA1 carriers and 34.6% for BRCA2 carriers. Conclusion The risk of contralateral breast cancer in women with a BRCA mutation is approximately 40% at 10 years, and is reduced in women who take tamoxifen or who undergo an oophorectomy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".