Perspective of young breast cancer survivors (BCS) on fertility preservation (FP) referral.
Bibliographic record
Abstract
113 Background: Despite ASCO recommendations (2006, 2013), young cancer patients are frequently not spoken to about the potential effects of treatment on future fertility and are denied the option of FP. Last year we began a 5 year study (SPOKE: Surgeon and Patient Oncofertility Knowledge Enhancement) to increase FP discussion and referral rates of Canadian breast surgeons. To design effective interventions, in addition to interviewing and surveying surgeons we conducted in-depth interviews with young BCS. Methods: Thirty minute semi-structured telephone interviews were conducted with BCS diagnosed < age 40 to explore their oncofertility experiences and their recommendations for future patients. Eligible BCS were recruited by oncologists and fertility specialists. A patient sample was deliberately chosen to reflect diversity of tumor stage, patient demographics and fertility experiences. A $20 incentive was given to each interviewee. Interviews were audio-recorded, transcribed verbatim and the data analyzed using qualitative methods. Subject recruitment was terminated when saturation of themes was obtained. Results: Fifteen BCS from 5 provinces, mean age 32 were interviewed. At diagnosis mean age was 29 with 8 (53%) single, 13 (87%) nulliparous, and 87% receiving adjuvant chemotherapy. At diagnosis all but 1 BCS had either definitely (60%) or possibly (33%) wanted (additional) children but only 8 of the 14 (57%) had been referred for a FP consult. Common recommendations were: discussing fertility as soon as possible after diagnosis to optimize time for decision-making; providing up-to-date information about the various FP options prior to the consult; and encouraging patients ambivalent about future childbearing to pursue FP. Conclusions: Despite the perception by many surgeons that patients are not psychologically able to handle information about fertility and FP at the time of their diagnosis, the majority of BCS interviewed thought that early fertility discussion by the surgical team was essential. SPOKE will include knowledge translations interventions for surgeons, and the creation of a FP information ‘toolbox’ with separate versions for surgeons and patients. Funded by the CBCF and CIHR.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".