Development of a decision aid for young canadians diagnosed with breast cancer who are at risk of infertility following cancer treatment.
Bibliographic record
Abstract
108 Background: Young breast cancer patients are at risk of temporary or permanent infertility following the administration of gonadotoxic cancer treatments. Currently patients do not feel they receive enough information to make informed fertility decisions before treatment. We aim to determine the fertility-related information health care providers and breast cancer survivors consider valuable to include in a Canadian decision aid (Can-DA) for young breast cancer patients by reviewing existing decision support resources. Methods: A qualitative descriptive approach was used to evaluate 6 decision support resources created in other jurisdictions. Using purposeful sampling, 8 multi-disciplinary health care providers and 8 breast cancer survivors from across Canada evaluated 1 to 2 decision support resources in structured interviews. Interviews were conducted in-person and by telephone from March to June 2016 and ranged in length from 30 to 90 minutes. Interviews were transcribed verbatim, organized in NVivo, and analyzed deductively using the pre-defined sections of the interview guide as a framework. Results: Each decision support resource had valuable components to adapt for the Can-DA. Participants valued the inclusion of Canadian-specific and accurate information on resources for additional support and the success rates and cost ranges of fertility preservation procedures. There were mixed views on the impact and value of including in-depth fertility information such as adoption and other fertility-related options after treatment. Discrepancies were also seen on the value of personal stories and an explicit values clarification exercise. There was consensus on the inclusion of only pertinent fertility-related information that does not replicate information in supplementary patient education material to avoid overwhelming patients. Conclusions: The evaluation provided valuable insight on the information and design features to consider for the Can-DA. Findings will be used in combination with the International Patient Decision Aid Standards criteria to ensure the Can-DA meets the fertility information needs of young breast cancer patients in Canada.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".