Gonadotropin releasing hormone (GnRH) antagonist administration to decrease the risk of ovarian hyperstimulation syndrome in GNRH agonist cycles triggered with human chorionic gonadotropin
Bibliographic record
Abstract
Abstract Purpose In Gonadotropin releasing hormone(GnRH) agonist IVF, after administration of human-chorionic-gonadotropin(HCG) triggering, there is a risk of ovarian hyperstimulation syndrome(OHSS). Few methods exist to prevent OHSS in these cases. Therefore, we investigated the use of a GnRH antagonist to decrease the risk of OHSS, due to its ability to decrease VEGF production and function. Method A retrospective cohort study of 171-IVF patients at risk for developing OHSS after a GnRH agonist cycle with HCG trigger was performed from 2011–2019. The patient population consisted of women with an unexpected exuberant response to stimulation based on ovarian reserve testing and were triggered with hCG. Women were converted to a freeze-all cycle and received either cabergoline 0.5mg orally alone for 7 days from the collection(Group 1, n = 123) or received cabergoline 0.5mg orally and ganirelix, 250 mcg SC for 7–10 days(Group 2, n = 48). Results: Group 1 had more cases of moderate and severe OHSS than group 2-(25% vs. 10% p = 0.03, and 52% vs. 25% p = 0.001 respectively). Group 1 reported more abdominal discomfort and bloating than group 2(91% vs. 65% p < 0.001) and the presence of free fluid was more frequent in group 1 than group 2(74% vs. 35% p < 0.001). Hemoconcentration and electrolyte disturbances were less severe in group 2 than in group 1 (p < 0.001 all cases). Conclusion In patients at high risk for developing OHSS after hCG trigger in a GnRH agonist cycle, the addition of GnRH antagonists in the luteal phase may reduce the risk of developing moderate and severe OHSS. The GnRH antagonist likely leads to more rapid luteolysis and down regulation of VEGF production and receptor response, thereby decreasing the hallmark increased vascular permeability.
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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 | 0.000 |
| 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".