Survival from breast cancer in women with a BRCA2 mutation by treatment
Bibliographic record
Abstract
BACKGROUND: The impact of various breast-cancer treatments on patients with a BRCA2 mutation has not been studied. We sought to estimate the impact of bilateral oophorectomy and other treatments on breast cancer-specific survival among patients with a germline BRCA2 mutation. METHODS: We identified 664 women with stage I-III breast cancer and a BRCA2 mutation by combining five different datasets (retrospective and prospective). Subjects were followed for 7.2 years from diagnosis to death from breast cancer. Tumour characteristics and cancer treatments were patient-reported and derived from medical records. Predictors of survival were determined using Cox proportional hazard models, adjusted for other treatments and for prognostic features. RESULTS: The 10-year breast-cancer survival for ER-positive patients was 78.9% and for ER-negative patients was 82.3% (adjusted HR = 1.23 (95% CI, 0.62-2.45, p = 0.55)). The 10-year breast-cancer survival for women who had a bilateral oophorectomy was 89.1% and for women who did not have an oophorectomy was 59.0% (adjusted HR = 0.45; 95% CI, 0.28-0.72, p = 0.001). The adjusted hazard ratio for chemotherapy was 0.83 (95% CI, 0.65-1.53: p = 0.56). CONCLUSIONS: For women with breast cancer and a germline BRCA2 mutation, positive ER status does not predict superior survival. Oophorectomy is associated with a reduced risk of death from breast cancer and should be considered in the treatment plan.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".