A high frequency of <i>BRCA</i> mutations in young black women with breast cancer residing in Florida
Bibliographic record
Abstract
BACKGROUND: Black women are disproportionately affected with triple-negative breast cancer and have relatively poor survival. To the authors' knowledge, it is not known to what extent differences in the clinical presentation of breast cancer between non-Hispanic white women and black women can be accounted for by the presence of mutations in the BRCA1 and BRCA2 genes. The authors sought to evaluate the frequency of BRCA pathogenic variants in a population-based sample of young black women with breast cancer. METHODS: Black women diagnosed with invasive breast cancer at age ≤50 years from 2009 to 2012 were recruited to the study through the Florida Cancer Registry. Participants underwent genetic counseling, completed a study questionnaire, and consented to release of their medical records. Saliva specimens were collected for BRCA sequencing and large rearrangement testing through multiplex ligation-dependent probe amplification. RESULTS: A DNA sample was evaluated for 396 women, 49 of whom (12.4%) had a mutation in BRCA1 or BRCA2. Eight recurrent mutations accounted for 49% of all pathogenic variants. CONCLUSIONS: To the authors' knowledge, the prevalence of BRCA mutations among the Florida-based sample of young black women with breast cancer in the current study exceeds that previously reported for non-Hispanic white women. It is appropriate to recommend BRCA testing in all young black women with invasive breast cancer.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".