Prevalence of <i><scp>BRCA1</scp></i> and <i><scp>BRCA2</scp></i> mutations in unselected breast cancer patients from Peru
Bibliographic record
Abstract
The prevalence of BRCA1 and BRCA2 mutations among breast cancer patients in Peru has not yet been explored. We enrolled 266 women with breast cancer from a National cancer hospital in Lima, Peru, unselected for age or family history. DNA was screened with a panel of 114 recurrent Hispanic BRCA mutations (HISPANEL). Among the 266 cases, 13 deleterious mutations were identified (11 in BRCA1 and 2 in BRCA2), representing 5% of the total. The average age of breast cancer in the mutation-positive cases was 44 years. BRCA1 185delAG represented 7 of 11 mutations in BRCA1. Other mutations detected in BRCA1 included: two 2080delA, one 943ins10, and one 3878delTA. The BRCA2 3036del4 mutation was seen in two patients. Given the relatively low cost of the HISPANEL test, one should consider offering this test to all Peruvian women with breast or ovarian 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".