Pattern Anlysis of Risk-Reducing Strategies in Unaffected Korean BRCA1/2 Mutation Carriers
Bibliographic record
Abstract
The lifetime risk of breast and ovarian cancer increases substantially for individuals with mutations in BRCA1/2. The evidence indicates that BRCA1/2 mutation carriers benefit from early cancer detection and prevention strategies. However, data on the patterns of risk-reducing interventions are lacking. This study investigated the patterns of surveillance and risk-reducing interventions among unaffected BRCA1/2 mutation carriers. A cohort of unaffected BRCA1/2 mutation carriers was identified from the Korean Hereditary Breast cAncer (KOHBRA) study database, and a telephone survey was conducted. The survey included questions on the incidence of new cancers, patterns of cancer (breast, ovarian, prostate, other) surveillance, chemoprevention, risk-reducing surgery, and reasons for participating in risk-reducing strategies. Between November 2016 and November 2020, 192 BRCA1/2 mutation carriers were contacted, of which 83 responded. After excluding 37 responders who refused to participate, 46 participants (15 males, 31 females) were included in the analysis. The mean ± SD follow-up time was 103 ± 17 months (median 107, range 68~154), and the mean ± SD age was 31 ± 8 years. Ten BRCA1/2 mutation carriers developed breast cancer, one developed ovarian cancer, and three developed other cancers. Six BRCA1/2 mutation carriers (19.4%) underwent annual breast cancer surveillance as recommended by guidelines, while none underwent ovarian or prostate cancer surveillance. Three carriers (9.7%) used chemoprevention for breast cancer. Risk-reducing salpingo-oophorectomy was performed on only one BRCA1/2 mutation carrier. The rates of breast/ovarian cancer surveillance, chemoprevention, and risk-reducing surgery were low among unaffected Korean BRCA1/2 mutation carriers. Given this cohort’s relatively high risk of developing breast cancer, strategies to encourage active participation in risk reduction are needed.
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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 | 0.002 |
| 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) | 0.001 | 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".