Counselling Framework for Germline BRCA1/2 and PALB2 Carriers Considering Risk-Reducing Mastectomy
Bibliographic record
Abstract
Female BRCA1/2 and PALB2 germline pathogenic variant carriers have an increased lifetime risk of breast cancer and may wish to consider risk-reducing mastectomy (RRM) for surgical prevention. Quantifying the residual lifetime risk and absolute benefit from RRM requires careful consideration of a patient’s age, pathogenic variant, and their personal history of breast or ovarian cancer. Historically, patients have been counselled that RRM does not necessarily prolong survival relative to high-risk surveillance, although recent studies suggest a possible survival benefit of RRM in BRCA1 carriers. The uptake of RRM has increased dramatically over the last several decades yet varies according to sociodemographic factors and geographic region. The increased adoption of nipple-sparing mastectomy techniques, ability to avoid axillary staging, and availability of reconstructive options for most germline pathogenic variant carriers has helped to minimize the morbidity of RRM. Preoperative discussions should include evidence regarding postmastectomy sensation, the potential for supplemental surgery, pregnancy-related chest wall changes, and the need for continued clinical surveillance. Approaches that include sensation preservation and robotic nipple-sparing mastectomy are an area of evolving research that may be more widely adopted in the future.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".