Preferences for breast cancer risk reduction among BRCA1/BRCA2 mutation carriers: a discrete-choice experiment
Bibliographic record
Abstract
PURPOSE: Unaffected women who carry BRCA1 or BRCA2 mutations face difficult choices about reducing their breast cancer risk. Understanding their treatment preferences could help us improve patient counseling and inform drug trials. The objective was to explore preferences for various risk-reducing options among women with germline BRCA1/2 mutations using a discrete-choice experiment survey and to compare expressed preferences with actual behaviors. METHODS: A discrete-choice experiment survey was designed wherein women choose between hypothetical treatments to reduce breast cancer risk. The hypothetical treatments were characterized by the extent of breast cancer risk reduction, treatment duration, impact on fertility, hormone levels, risk of uterine cancer, and ease and mode of administration. Data were analyzed using a random-parameters logit model. Women were also asked to express their preference between surgical and chemoprevention options and to report on their actual risk-reduction actions. Women aged 25-55 years with germline BRCA1/2 mutations who were unaffected with breast or ovarian cancer were recruited through research registries at five clinics and a patient advocacy group. RESULTS: Between January 2015 and March 2016, 622 women completed the survey. Breast cancer risk reduction was the most important consideration expressed, followed by maintaining fertility. Among the subset of women who wished to have children in future, the ability to maintain fertility was the most important factor, followed by the extent of risk reduction. Many more women said they would take a chemoprevention drug than had actually taken chemoprevention. CONCLUSIONS: Women with BRCA1/2 mutations indicated strong preferences for breast cancer risk reduction and maintaining fertility. The expressed desire to have a safe chemoprevention drug available to them was not met by current chemoprevention options.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".