Development of decision aids for female BRCA1 and BRCA2 mutation carriers in Germany to support preference-sensitive decision-making
Bibliographic record
Abstract
BACKGROUND: Women with pathogenic BRCA1 and BRCA2 mutations possess a high risk of developing breast and ovarian cancer. They face difficult choices when considering preventive options. This study presents the development process of the first decision aids to support this complex decision-making process in the German healthcare system. METHODS: A six-step development process based on the International Patient Decision Aid Standards was used, including a systematic literature review of existing decision aids, a topical medical literature review, preparation of the decision aids, focus group discussions with women with BRCA1/2 mutations, internal and external reviews by clinical and self-help experts, and user tests. All reviews were followed by iterative revisions. RESULTS: No existing decision aids were transferable to the German setting. The medical research revealed a need to develop separate decision aids for women with BRCA1/2 mutations (A) without a history of cancer (previvors) and (B) with a history of unilateral breast cancer (survivors). The focus group discussions confirmed a high level of approval for the decision aids from both target groups. Additionally, previvors requested more information on risk-reducing breast surgery, risk-reducing removal of both ovaries and Fallopian tubes, and psychological aspects; survivors especially wanted more information on breast cancer on the affected side (e.g. biological parameters, treatment, and risk of recurrence). CONCLUSIONS: In a structured process, two target-group-specific DAs for previvors/survivors with BRCA1/2 mutations were developed to support decision-making on risk-adapted preventive options. These patient-oriented tools offer an important addition to existing specialist medical care in Germany.
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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 | 0.041 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| 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".