Is There a Role for Risk-Reducing Bilateral Breast Surgery in BRCA1/2 Ovarian Cancer Survivors? An Observational Study
Bibliographic record
Abstract
Background: Risk-reducing surgeries are an option for cancer risk management in BRCA1/2 individuals. However, while adnexectomy is commonly recommended in breast cancer (BC) survivors, risk-reducing bilateral breast surgery (RRBBS) is controversial in ovarian cancer (OC) survivors due to relapse rates and mortality. Methods: We conducted a retrospective analysis of BRCA1/2-OC survivors, with OC as first cancer diagnosis. Results: Median age at OC diagnosis for the 69 BRCA1/2-OC survivors was 54 years. Median overall survival was 8 years, being significantly higher for BRCA2 patients than for BRCA1 patients (p = 0.011). Nine patients (13.2%) developed BC at a median age of 61 years. The mean overall BC-free survival was 15.5 years (median not reached). Eight patients (11.8%) underwent bilateral mastectomy (5 simultaneous with BC treatment; 3 RRBBS) at a median age of 56.5 years. The median time from OC to bilateral mastectomy/RRBBS was 5.5 years. Conclusions: This study adds evidence regarding a lower BC risk after BRCA1/2-OC and higher survival for BRCA2-OC patients. A comprehensive analysis of the competing risks of OC mortality and recurrence against the risk of BC should be individually addressed. Surgical BC risk management may be considered for longer BRCA1/2-OC disease-free survivors. Ultimately, these decisions should always be tailored to patients’ characteristics and preferences.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".