Development and pilot testing of a Decision Aid (DA) for women with early-stage breast cancer considering contralateral prophylactic mastectomy
Bibliographic record
Abstract
© 2018 Elsevier Ltd Objective: Describe the development, acceptability and feasibility of a Decision Aid (DA) for women with early-stage breast cancer (BC) at average contralateral breast cancer (CBC) risk considering contralateral prophylactic mastectomy (CPM). Methods: The DA was developed using the International Patient Decision Aid Standards (IPDAS) and the Ottawa Decision Support Framework. It provides evidence-based information about CPM in a booklet format combining text, graphs and images of surgical options. Twenty-three women with a history of early-stage breast cancer were interviewed in person or over the phone using a ‘think aloud approach’. Framework analysis was used to code and analyse data. Results: Twenty-three women participated in the study. Mean age of participants was 58.6 years and time since diagnosis ranged from 14 months to 21 years. Five women had CPM and eighteen had not. Women strongly endorsed the DA. Many felt validated by a section on appearance and found information on average risk of recurrence and metastases helpful, however, noted the importance of discussing personal risk with their surgeon. Many requested more information on surgery details (time taken, recovery) and costs of the different options. Conclusion: The DA was acceptable to women, including the format, content and proposed implementation strategies. Practical and financial issues are important to women in considering treatment options. Practice implications: Women appreciate information about CPM at diagnosis and emphasised the importance of discussing potential downsides of the procedure in addition to benefits. The DA was considered acceptable to facilitate such discussions.
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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 | 0.025 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".