<i>BRCA1/2</i> in High-Risk African American Women with Breast Cancer: Providing Genetic Testing through Various Recruitment Strategies
Bibliographic record
Abstract
BACKGROUND: Due to the disproportionate numbers of African American women affected with early onset breast cancer, we sought to investigate mutation frequency of BRCA1 and BRCA2 (BRCA1/2) in a sample of African American women, recruited through a variety of methods. METHODS: We conducted a study investigating BRCA1/2 among 51 African American breast cancer patients with a personal or family history suggestive of hereditary predisposition to breast cancer. All individuals underwent genetic counseling and BRCA1/2 mutation analysis, through protein-truncation test on exon 11 of BRCA1 and exons 10 and 11 of BRCA2, which together account for approximately 50% of mutations observed within these genes. RESULTS: Of the 51 women tested for BRCA1/2 mutations, 3 were identified as mutation carriers (5.9%), including 1 in BRCA1 and 2 in BRCA2. Recruitment strategies varied and included physician referrals from the Moffitt Cancer Center Breast Program (18), community-based oncologists (13), primary care physicians (3), newspaper advertisements and brochures (5), community or support group referrals (7), self/family referral through word of mouth (2), and the Florida State Cancer Registry (3). CONCLUSIONS: Our results suggest that (1) BRCA1/2 mutations are seen in high-risk African American women with breast cancer, and (2) strategies for recruitment of African American women in studies of genetic testing for breast cancer genes have varied levels of success. Our study highlights the need for further studies in this population group.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".