The "Begin Exploring Fertility Options, Risks and Expectations" (BEFORE) decision aid: development and alpha testing of a fertility tool for premenopausal breast cancer patients
Bibliographic record
Abstract
BACKGROUND: Premenopausal breast cancer patients are at risk of treatment-related infertility. Many patients do not receive sufficient fertility information before treatment. As such, our team developed and alpha tested the Begin Exploring Fertility Options, Risks, and Expectations decision aid (BEFORE DA). METHODS: The BEFORE DA development process was guided by the International Patient Decision Aids Standards and the Ottawa Decision Support Framework. Our team used integrated knowledge translation by collaborating with multiple stakeholders throughout the development process including breast cancer survivors, multi-disciplinary health care providers (HCPs), advocates, and cancer organization representatives. Based on previously conducted literature reviews and a needs assessment by our team - we developed a paper prototype. The paper prototype was finalized at an engagement meeting with stakeholders and created into a graphically designed paper and mirrored online decision aid. Alpha testing was conducted with new and previously engaged stakeholders through a questionnaire, telephone interviews, or focus group. Iterative reviews followed each step in the development process to ensure a wide range of stakeholder input. RESULTS: Our team developed an 18-page paper prototype containing information deemed valuable by stakeholders for fertility decision-making. The engagement meeting brought together 28 stakeholders to finalize the prototype. Alpha testing of the paper and online BEFORE DA occurred with 17 participants. Participants found the BEFORE DA usable, acceptable, and most provided enthusiastic support for its use with premenopausal breast cancer patients facing a fertility decision. Participants also identified areas for improvement including clarifying content/messages and modifying the design/photos. The final BEFORE DA is a 32-page paper and mirrored online decision aid ( https://fertilityaid.rethinkbreastcancer.com ). The BEFORE DA includes information on fertility, fertility options before/after treatment, values clarification, question list, next steps, glossary and reference list, and tailored information on the cost of fertility preservation and additional resources by geographic location. CONCLUSION: The BEFORE DA, designed in collaboration with stakeholders, is a new tool for premenopausal breast cancer patients and HCPs to assist with fertility discussions and decision-making. The BEFORE DA helps to fill the information gap as it is a tool that HCPs can refer patients to for supplementary information surrounding fertility.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".