<i>BRCA</i> Mutations in Women with Ductal Carcinoma <i>In situ</i>
Bibliographic record
Abstract
PURPOSE: The strength of the association between ductal carcinoma in situ (DCIS) and BRCA mutations has not been defined. EXPERIMENTAL DESIGN: Mutation frequency was compared in three groups: (1) a prevalent series of women with DCIS, (2) an incident series of women with DCIS, and (3) a clinic-based series of women with DCIS referred for hereditary cancer risk assessment. In groups 1 and 2, limited to Ashkenazi Jewish (AJ) cases, mutation frequency was compared with that in age-matched AJ controls with invasive breast cancer (IBC). RESULTS: In group 1, 3 of 62 (4.8%) women with DCIS and 15 of 130 (11.5%) controls with IBC had BRCA mutations. In group 2, 0 of 58 (0%) women with DCIS and 6 of 116 (5.2%) controls with IBC had BRCA mutations [combined odds ratios (OR) in groups 1 and 2: 3.64, 95% confidence interval (95% CI), 1.06-12.46; P=0.04]. In group 3, deleterious mutations were identified in 10 of 79 (12.7%) probands with DCIS, similar to the frequency in IBC probands. In group 3, mutations were associated with family history of ovarian cancer (OR, 13.35; 95% CI, 2.48-71.94; P=0.003) or early onset breast cancer (OR, 16.23; 95% CI, 1.68-157.01; P=0.02) but not with AJ ethnicity or age at diagnosis. CONCLUSIONS: BRCA mutations were less frequent in women with DCIS not selected for family history or age at diagnosis than in women with IBC. Nonetheless, mutations were found in a significant proportion of women with DCIS who presented for hereditary risk assessment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".