Development and testing of a decision aid for breast cancer prevention for women with a BRCA1 or BRCA2 mutation
Bibliographic record
Abstract
For women who carry a mutation in BRCA1 or BRCA2, the risk of breast cancer is up to 87% by the age of 70. There are options available to reduce the risk of breast cancer; however, each option has both risks and benefits, which makes decision making difficult. The objective is to develop and pilot test a decision aid for breast cancer prevention for women with a BRCA1 or BRCA2 mutation. The decision aid was developed and evaluated in three stages. In the first stage, the decision aid was developed and reviewed by cancer genetics experts. The second stage was a review of the decision aid by women with a BRCA1 or BRCA2 mutation for acceptability and feasibility. The final stage was a pre-test--post-test evaluation of the decision aid. Twenty-one women completed the pre-test questionnaire and 20 completed the post-test questionnaire. After using the decision aid, there was a significant decline in mean decisional conflict scores (p = 0.001), a significant improvement in knowledge scores (p = 0.004), and fewer women uncertain about prophylactic mastectomy (p = 0.003) and prophylactic oophorectomy (p = 0.009). Use of the decision aid decreased decisional conflict to levels suggestive of implementation of a decision. In addition, knowledge levels increased and choice predisposition changed with fewer women being uncertain about each option. This has significant clinical implications as it implies that with greater uptake of cancer prevention options by women with a BRCA1 or BRCA2 mutation, fewer women will develop and/or die of hereditary breast cancer.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".