Prophylactic Surgery in the BRCA+ Patient: Do Women Develop Breast Cancer While Waiting?
Bibliographic record
Abstract
Breast cancer susceptibility gene (BRCA) mutation carriers have an increased risk of breast cancer. Mitigation of this risk can be achieved via surveillance or prophylactic mastectomy with or without breast reconstruction. Those that choose surgery expect to reduce their chance of developing cancer. The purpose of this study was to determine the incidence of patients developing breast cancer prior to surgery and to identify modifiable contributing factors within the patient journey. This is a historical cohort study of all BRCA mutation carriers identified through the British Columbia Cancer Hereditary Cancer Program between 2000 and 2012. Patients were divided into two groups: surveillance (S) and prophylactic mastectomy with immediate breast reconstruction (PM/IBR). The incidence of cancer, time to PM/IBR and patient journeys were analyzed. A total of 333 women were identified. The time to surgery from mutation disclosure was a median of 31 (5.3, 75.7) months. During this period, 6% of patients developed breast cancer compared with a 14% incidence of breast cancer in patients choosing surveillance. The majority of time to surgery was attributed to the period between mutation disclosure and the decision to proceed with surgery. Strategies to facilitate decision-making as well as wait list prioritization and dedicated operative time should be targeted to this population to decrease the number of women developing an interval cancer prior to surgery.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".