Does the timing of cabergoline administration impact rates of ovarian hyperstimulation syndrome?
Bibliographic record
Abstract
OBJECTIVE: Does the timing of cabergoline administration impact the rate of mild/moderate ovarian hyperstimulation syndrome in women with a GnRH agonist trigger? METHODS: We conducted a retrospective cohort analysis of 285 in-vitro fertilization patients at risk of OHSS who received a GnRH agonist trigger from 2011 to 2019 at McGill University Health Centre. Group 1 (Trig, n=101) began taking cabergoline 0.5 mg orally for 7 days at the time of GnRH agonist trigger, while Group 2 (Retriev, n=184) started taking cabergoline on the day of oocyte retrieval. The rates of OHSS were then compared between the groups using analysis of variance and chi-square analysis, where appropriate. RESULTS: The baseline demographic characteristics of the two groups were similar. Trig appeared to be at a slightly higher risk of OHSS based on a significantly higher antral follicle count (20.2±4.2 vs. 19.0±4.3; P=0.02), higher number of stimulated follicles >10 mm at trigger (25.7±7.0 vs. 22.8±8.3, P=0.003), and higher peak serum E2 level (17,325±2,542 vs. 14,822±3,098; P=0.0001). The Trig group had lower rates of mild and moderate OHSS (24% vs. 36%; P=0.045). Neither group had any patients who developed severe OHSS. Trig had fewer patients presenting with pelvic free fluid (13% vs. 23%; P=0.03), lower hematocrit (37.8±4.8% vs. 40.5±4.2%; P=0.0001), higher albumin concentrations (30.4±2.7 vs. 29.5±2.0; P=0.01), and lower potassium concentrations (3.9±0.5 vs. 4.2±0.7; P=0.0002). CONCLUSION: Cabergoline at the time of trigger as compared to the time of collection should be investigated to assess its role in reducing the rates of mild/moderate OHSS.
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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 | 0.006 |
| 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".