Development and Evaluation of a Decision Aid for <i>BRCA</i> Carriers with Breast Cancer
Bibliographic record
Abstract
BRCA+ breast cancer patients face high risk for a second breast cancer and ovarian cancer. Helping these women decide among risk-reducing options requires effectively conveying complex, emotionally-laden, information. To support their decision-making needs, we developed a web-based decision aid (DA) as an adjunct to genetic counseling. Phase 1 used focus groups to determine decision-making needs. These findings and the Ottawa Decision Support Framework guided the DA development. Phase 2 involved nine focus groups of four stakeholder types (BRCA+ breast cancer patients, breast cancer advocates, and genetics and oncology professionals) to evaluate the DA's decision-making utility, information content, visual display, and implementation. Overall, feedback was very favorable about the DA, especially a values and preferences ranking-exercise and an output page displaying personalized responses. Stakeholders were divided as to whether the DA should be offered at-home versus only in a clinical setting. This well-received DA will be further tested to determine accessibility and effectiveness.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".