Protocol-Specific Outcomes of GnRH Agonist Use in Luteal Phase Support During Frozen Embryo Transfer Cycles
Bibliographic record
Abstract
Purpose: Recent data comparing natural cycles and artificial cycles in frozen embryo transfer (FET) showed an equivalent LBR when optimized luteal phase support (LPS) was used. Of the suggested methods is the use of GnRH agonists as part of LPS. We aim to study whether the addition of GnRH agonists as LPS in FET cycles increases the live birth rate (LBR) and decreases the miscarriage rate (MR). Methods: A retrospective analysis was performed for 140 FET cycles, which were divided into two groups. The study group in which a GnRH agonist was used (AG) at the time of embryo transfer included 66 cycles, whereas the control group (NAG) included 74 cycles in which the use of GnRH agonist was not described. Results: The implantation rate was greater in the AG (69/112 (61.6%) vs 60/124 (48.4%), p= 0.0413). The LBR was greater in the AG than in the CG but did not reach statistical significance (40/66 (60.6%) vs 35/74 (47.3%), p= 0.114). The MR was similar between the 2 groups (6/66 (10%) vs 5/74 (6.7%), p= 0.61). The subanalysis per FET protocol revealed that there was no difference in the LBR between the AG-medicated and NAG-medicated cycles (15/34 (44.62%) vs (36/65 (55.38%), p=0.1984) or between the AG-ovulation induction and NAG-ovulation induction FET cycles (21/32 (65.63%) vs 6/9 (66.67%), p=0.1985). Conclusion: The use of a GnRH agonist as an add-on for LPS in FET cycles numerically increased the LBR without reaching statistical significance despite significantly improving the implantation rate. MR were not affected. This potential beneficial effect was comparable between the artificial and ovulation-induction FET cycles.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".