Management of <i>BRCA1/2</i> mutation carriers
Bibliographic record
Abstract
Introduction The application of predictive genetics in oncology offers the opportunity, through a simple blood draw, to identify asymptomatic individuals carrying a predisposition to develop certain cancers. Specific recommendations regarding prevention and surveillance can be proposed to these individuals. Concomitantly, family members who are identified as non-carriers of the predisposing gene can be reassured. These persons are no longer considered ‘at high risk’ and return to the cancer risks of the general population. They can be withdrawn from often demanding screening protocols and can be reassured regarding the absence of risk of transmission of the predisposition to their children. Here, we provide an overview of the options and issues in the management of women identified as being at high risk of developing breast or ovarian cancer, based on their personal and familial history, or through the identification of a germline BRCA1 or BRCA2 mutation. Breast and ovarian cancers combined account for about one-third of all incident cancers in Canadian women, and for about one-fourth of all cancer deaths (Table 15.1). Primary care for survivors of sporadic breast cancer has been recently reviewed (Burstein and Winer, 2000), but women carrying genetic predisposition to breast/ovarian cancer have unique health issues. Approximately 3% of all breast cancer and 5–10% of all ovarian cancer is caused by germline mutations in breast/ovarian cancer susceptibility genes. The most important of these genes are BRCA1 and BRCA2 , which were identified in 1994 and 1995 respectively (Rahman and Stratton, 1998; Welcsh et al., 1998).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".