Increased cancer risks for relatives of very early-onset breast cancer cases with and without BRCA1 and BRCA2 mutations
Bibliographic record
Abstract
BACKGROUND: Little is known regarding cancer risks for relatives of women with very early-onset breast cancer. METHODS: We studied 2208 parents and siblings of 504 unselected population-based Caucasian women with breast cancer diagnosed before age 35 years (103 from USA, 124 from Canada and 277 from Australia), 41 known to carry a mutation (24 in BRCA1, 16 in BRCA2 and one in both genes). Cancer-specific standardised incidence ratios (SIRs) were estimated by comparing the number of affected relatives (50% verified overall) with that expected based on incidences specific for country, sex, age and year of birth. RESULTS: For relatives of carriers, the female breast cancer SIRs were 13.13 (95% CI 6.57-26.26) and 12.52 (5.21-30.07) for BRCA1 and BRCA2, respectively. The ovarian cancer SIR was 12.38 (3.1-49.51) for BRCA1 and the prostate cancer SIR was 18.55 (4.64-74.17) for BRCA2. For relatives of non-carriers, the SIRs for female breast, prostate, lung, brain and urinary cancers were 4.03 (2.91-5.93), 5.25 (2.50-11.01), 7.73 (4.74-12.62), 5.19 (2.33-11.54) and 4.35 (1.81-10.46), respectively. For non-carriers, the SIRs remained elevated and were statistically significant for breast and prostate cancer when based on verified cancers. CONCLUSION: First-degree relatives of women with very early-onset breast cancer are at increased risk of cancers not explained by BRCA1 and BRCA2 mutations.
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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 | 0.003 |
| 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 | 0.000 |
| 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".