Challenges and Considerations on Risk-Reducing Surgery in BRCA1/2 Patients with Advanced Breast Cancer
Bibliographic record
Abstract
Cancer survivors harboring inherited pathogenic variants in the breast cancer (BC) susceptibility genes BRCA1 or BRCA2 are at increased risk of ovarian cancer (OC) and also of contralateral BC. For these women, risk-reducing surgery (RRS) may contribute to risk management. However, women with locally advanced or metastatic breast cancer (ABC) were excluded from clinical trials evaluating the benefit of these procedures in the BRCA1/2 carriers, and thus, current guidelines do not recommend RRS in this specific setting. Although ABC remains an incurable disease, recent advances in treatment have led to increased survival, which, together with improvement in RRS techniques, raise questions about the potential role of RRS in the management of BRCA1/2 ABC patients. When should RRS be discussed as an option for BRCA1/2 patients diagnosed with ABC? To address this issue, we report two clinical cases that reflect new challenges in routine oncology practice. Team experience and patient motivations may shape multidisciplinary decisions in the absence of evidence-based data. A wise rationale may be the analysis of the competing risks of death by a previous ABC against risk of death by a secondary BC or OC, tailored to patient 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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".