A Retrospective Study on the Use of High-Dose Letrozole While Undergoing Ovarian Stimulation for Oocyte and Embryo Cryopreservation in Cancer Patients
Bibliographic record
Abstract
Objective: To determine the efficacy of letrozole in suppressing estradiol levels during ovarian stimulation in cancer patients. Methods: A retrospective chart review of cancer patients undergoing ovarian stimulation for fertility preservation between 2014-2019 at a private university-affiliated fertility clinic in Canada was conducted. Ovarian stimulation was completed with no letrozole (Group A, n = 10), and adjuvant daily letrozole use at 5.0 (Group B, n = 34) or 7.5 mg (Group C, n = 61). The primary outcomes were peak estradiol levels and oocyte yield. ANOVA with a post hoc two-tailed t-test assuming equal variance was utilized as a statistical method. Result(s): Patient age and AFC count were not different between groups. The yield of mature eggs was not different at each letrozole dose; 9.2 ± 6.0, 13.9 ± 6.5 and 12.7 ± 7.2 for Groups A to C respectively (p = 0.18). Mean estradiol levels(pmol/L) were reduced in a dose-dependent manner; 7432 ± 4553 for Group A, 2072 ± 1656 for Group B, and 1445 ±1238 for Group C (A vs. C, p vs. C, p Conclusion(s): The use of letrozole during ovarian stimulation for oocyte and embryo cryopreservation in cancer patients can maintain physiologic estradiol levels, while ensuring satisfactory oocyte and embryo yield. Letrozole can, therefore, minimize the theoretical risk of stimulating residual and metastatic diseases, while still optimizing future fertility outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".