The Voices of Young Women with Breast Cancer: Providing Support and Information for Improved Fertility Preservation Discussions
Bibliographic record
Abstract
Purpose: Fertility is a concern for young women with breast cancer. We explore patient experiences with fertility discussions at diagnosis to identify barriers and preferences to patient-centered delivery of care. Methods: A qualitative study was conducted on consecutive, female breast cancer survivors, 39 years of age or younger at diagnosis and within 2 years of diagnosis, who attended routine outpatient follow-up at a large academic teaching center. Interviews lasted 30 minutes and were transcribed verbatim. Thematic analysis was conducted to explore experiences around fertility discussions. Strength of the theme was determined by examining the frequency of a response. Data collection and analysis continued until theoretical saturation was reached. Results: Analysis comprised 50 women with a median age of 34.5 years (range 25–39 years). Thirty-nine women (78%) had completed university education. Thirty-three women (66%) recalled having fertility preservation discussions at diagnosis. The most common themes identified include the following: (i) the requirement for more patient support, (ii) improving information, (iii) integration of patient values, (iv) creating options for patients, (v) financial limitations, and (vi) the need to look beyond the immediate impact. Conclusions: In this contemporary cohort of young adult breast cancer survivors, fertility discussion experiences at diagnosis remain suboptimal. Improved information and a focus on individual patient desires can improve experiences.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".