Predictors of contralateral breast cancer in BRCA1 and BRCA2 mutation carriers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: The objective of this study was to estimate the risk of contralateral breast cancer in BRCA1 and BRCA2 carriers; and measure the extent to which host, family history, and cancer treatment-related factors modify the risk. PATIENTS AND METHODS: Patients were 810 women, with stage I or II breast cancer, for whom a BRCA1 or BRCA2 mutation had been identified in the family. Patients were followed from the initial diagnosis of cancer until contralateral mastectomy, contralateral breast cancer, death, or last follow-up. RESULTS: Overall, 149 subjects (18.4%) developed a contralateral breast cancer. The 15-year actuarial risk of contralateral breast cancer was 36.1% for women with a BRCA1 mutation and was 28.5% for women with a BRCA2 mutation. Women younger than 50 years of age at the time of breast cancer diagnosis were significantly more likely to develop a contralateral breast cancer at 15 years, compared with those older than 50 years (37.6 vs 16.8%; P=0.003). Women aged <50 years with two or more first-degree relatives with early-onset breast cancer were at high risk of contralateral breast cancer, compared with women with fewer, or no first-degree relatives with breast cancer (50 vs 36%; P=0.005). The risk of contralateral breast cancer was reduced with oophorectomy (RR 0.47; 95% CI 0.30-0.76; P=0.002). CONCLUSION: The risk of contralateral breast cancer risk in BRCA mutation carriers declines with the age of diagnosis and increases with the number of first-degree relatives affected with breast cancer. Oophorectomy reduces the risk of contralateral breast cancer in young women with a BRCA mutation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it