Investigations of the quality of fertility preservation education provided to patients with cancer at a large community hospital.
Bibliographic record
Abstract
e23087 Background: Infertility is a challenge many young cancer patients face as a consequence of aggressive treatments. Moreover, many eligible patients do not receive discussion about their fertility, and even fewer obtain referrals to fertility specialists. The goal of the investigation was to examine the frequency and quality of fertility preservation education provided to eligible patients at the Windsor Regional Cancer Program (WRCP). Methods: The first part of the investigation involved a retrospective chart review which identified 203 patients that presented to the WRCP between January 15, 2018 to January 15, 2020 under the age of 40. Amongst these patients, we identified 54 patients that were eligible for fertility preservation. These 54 patients were contacted to participate in the survey and we received 11 responses. The survey data collected include demographics and experience with fertility preservation. The data were analyzed using University of Windsor RedCap. Windsor Regional Hospital REB approval number: 22-426. Results: The retrospective chart review revealed that 34% of the 54 eligible patients did not receive documented fertility preservation counseling. Amongst the survey respondents that had fertility preservation discussions, 86% (6 patients) cited that it was initiated by a healthcare provider. In addition, we found that only 27% (3 patients) of survey respondents were interested in pursuing fertility preservation. The reasoning cited most often included not wanting children, already had children, and barriers such as finances, time and effort to pursue fertility preservation and fear of delaying fertility preservation. Moreover, the 3 survey respondents successfully pursued fertility preservation in the form of sperm collection (2 patients) and freezing eggs/embryos (1 patient). Conclusions: We identified the need to improve standardization of fertility preservation education in eligible patients. Moreover, we found that few patients desired fertility preservation. Although this is a small number of patients, providing appropriate fertility education is essential in cancer survivorship. Our next steps include piloting and implementing an improved fertility education program.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".