Timing of <i>BRCA</i> Genetic Testing and Surgical Decision-Making Among Young Black Women With Breast Cancer
Bibliographic record
Abstract
Introduction Genetic testing for hereditary cancer syndromes, particularly BRCA1 and BRCA2 ( BRCA ) germline pathogenic or likely pathogenic variants (GPVs), is critical in informing surgical decisions for women with breast cancer. Young Black women are historically underrepresented in genetic testing and research, making it essential to understand how testing timing influences treatment choices. We evaluated how the timing of BRCA testing affected surgical management among young Black women with breast cancer. Methods Participants were drawn from a population-based cohort of Black women diagnosed with invasive breast cancer at age 50 or younger, recruited via Florida and Tennessee cancer registries. Data were collected through structured questionnaires, electronic health records, and lab reports, including information on genetic testing, BRCA status, and treatment. Participants were categorized by timing of BRCA testing (pre-surgical vs post-surgical) and GPV status. Chi-squared tests assessed associations between testing timing, BRCA status, and surgical treatment. Results Among 633 participants, people with a BRCA GPV who were tested before surgery (n = 29) were significantly more likely to undergo bilateral mastectomy (82.8%) than those tested after surgery (40%). Timing of testing and BRCA status were both strongly associated with surgery received ( P < 0.0001). Conclusion BRCA testing at diagnosis and prior to surgery is significantly associated with surgical management in young Black women with breast cancer. These findings highlight the importance of timely genetic testing, especially in populations with historically lower testing rates.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".