Impact of a Nurse Navigator Program on Referral Rates and Use of Fertility Preservation Among Female Cancer Patients: A 14‐Year Retrospective Cohort Study
Bibliographic record
Abstract
INTRODUCTION: Given the known detrimental impact of cancer treatment on fertility, fertility preservation (FP) is recommended for reproductive age patients who are newly diagnosed with cancer. However, the rate of referral to fertility specialists remains suboptimal. The objective of this study was to determine the impact of a dedicated Nurse Navigator Program (NNP) on the rate of referrals and utilization of FP services. METHODS: A retrospective cohort study of all women ≥ 18 years old referred for FP consultation with a known cancer diagnosis from 2007 to 2021 at a single, large academic center was conducted. FP referrals for non-cancer indications were excluded. Descriptive statistics were performed including comparing referrals received per 30 days and FP utilization rates pre-NNP (October 2007-September 2013) to post-NNP (October 2013-December 2021). RESULTS: A total of 176 patients were included pre-NNP and 990 patients post-NNP. Overall, the mean age at the time of referral was 31.5 ± 6.9 years. The referral rates post-NNP were higher among those without prior exposure to chemotherapy/radiation (0.33 pre-NNP vs. 2.75 post-NNP per 30 days, p < 0.01) and lower among those with prior exposure to chemotherapy/radiation (1.26 pre-NNP vs. 0.70 post-NNP per 30 days, p < 0.01). CONCLUSIONS: After the launch of a dedicated fertility preservation nurse navigation program at our institution, we observed a higher number of referrals for FP as well as greater use of FP overall. While not the only variable that changed during this period, this program has optimized patient care and clinical workflow at our institution and serves as a model for such improvement.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".