Early Onset Breast Cancer in a Registry-based Sample of African-American Women:<i>BRCA</i>Mutation Prevalence, and Other Personal and System-level Clinical Characteristics
Bibliographic record
Abstract
Young Black women are disproportionately afflicted with breast cancer, a proportion of which may be due to BRCA1 and BRCA2 (BRCA) gene mutations. In a sample of Black women with early onset breast cancer, we evaluated BRCA mutations and explored personal and system-level clinical characteristics. Black women diagnosed with invasive breast cancer (age ≤50) were recruited through the state cancer registry. Participants completed a questionnaire, genetic counseling and BRCA testing. Of the 48 women who consented to study participation, 46 provided a usable biologic specimen for BRCA testing. The overall prevalence of BRCA mutations and variants of uncertain significance (VUS) in participants was 6.5% and 34.8%, respectively. Of these, only 14 were referred for genetic counseling prior to study enrollment. Overall, those participants who chose to undergo bilateral mastectomy had a higher number of relatives with breast and ovarian cancer (p = 0.024) and a higher household income (p = 0.009). BRCA mutation prevalence and the high prevalence of VUS in participants are consistent with prior studies. Furthermore, clinical factors such as family history and financial means may influence type of surgery recommended and chosen, at both the provider and patient level, respectively. Finally, the limited number of patients referred for genetic counseling prior to surgical treatment for breast cancer may represent a missed clinical opportunity to inform surgical decisions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".