An evaluation of oncofertility decision support resources among breast cancer patients and health care providers
Bibliographic record
Abstract
BACKGROUND: Cancer patients of reproductive age are at risk of infertility as a result of their treatment. Oncofertility decision support resources can assist patients with fertility decision-making before treatment yet available oncofertility resources contain varying levels of detail and different fertility options. The key information/sections needed in oncofertility resources remain unclear. To explore the information needs for oncofertility decision-making before cancer treatment, we aimed to evaluate existing oncofertility decision support resources with breast cancer patients and providers. METHODS: We conducted 30 to 90-min interviews that included a survey questionnaire and open-ended questions with patients and providers between March and June 2016. Interviews were transcribed verbatim. Analysis involved descriptive statistics for survey responses and thematic analysis of qualitative data. RESULTS: A total of 16 participants completed interviews. Key information perceived by most participants as necessary for fertility decision-making included tailored post-treatment pregnancy rates, cost ranges and financial assistance for the fertility options based on patients' situation. However, patient and provider participants expressed differing opinions on the inclusion of all before and after treatment fertility options and the amount of fertility information required at diagnosis. CONCLUSION: The evaluation identified fertility information needs among patients in addition to providers' views on patient needs. While existing oncofertility resources contain information perceived as necessary for decision-making there is an opportunity to use these findings to create or enhance resources to better meet the needs of patients. Additionally, patients and providers differing views on information needs highlight the opportunity for provider training to ensure better communication using resources in clinic to understand specific patient needs.
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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 | 0.009 | 0.000 |
| 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".