A Prior Diagnosis of Breast Cancer is a Risk Factor for Breast Cancer in Brca1 and Brca2 Carriers
Bibliographic record
Abstract
BACKGROUND: The risk of breast cancer in carriers of BRCA1 and BRCA2 mutations is influenced by factors other than the genetic mutation itself. Modifying factors include a woman's reproductive history and family history of cancer. Risk factors are more likely to be present in women with breast cancer than in women without breast cancer, and therefore the risk of cancer in the two breasts should not be independent. It is not clear to what extent modifying factors influence the risk of a first primary or a contralateral breast cancer in BRCA carriers. METHODS: We conducted a matched case-control study of breast cancer among 3920 BRCA1 or BRCA2 mutation carriers. We asked whether a past history of breast cancer in the contralateral breast was a risk factor for breast cancer. RESULTS: After adjustment for age, country of residence, and cancer treatment, a previous cancer of the right breast was found to be a significant risk factor for cancer of the left breast among BRCA1 or BRCA2 carriers (relative risk: 2.1; 95% confidence interval: 1.4 to 3.0; p < 0.0001). CONCLUSIONS: In a woman with a BRCA1 or BRCA2 mutation who is diagnosed with breast cancer, the risk of cancer in the contralateral breast depends on the first diagnosis. That observation supports the hypothesis that there are important genetic or non-genetic modifiers of cancer risk in BRCA carriers. Discovering risk modifiers might lead to greater personalization of risk assessment and management recommendations for BRCA-positive patients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".