GnRH antagonists are safer than agonists: an update of a Cochrane review
Bibliographic record
Abstract
GnRH agonists or antagonists can be used to prevent LH surges during ovarian stimulation for assisted reproduction. GnRH agonists down-regulate GnRH pituitary receptors. GnRH antagonists directly and rapidly inhibit gonadotrophin release. In a 2006 systematic review involving 29 trials, the average clinical pregnancy rate was 4.7% lower with GnRH antagonist treatment and the incidence of ovarian hyperstimulation syndrome (OHSS) was 2% lower compared with GnRH agonist treatment (Al-Inany et al., 2006). The current update includes 45 trials which addressed live birth or ongoing pregnancy rate (OPR) in GnRH antagonist and GnRH agonist protocols among women undergoing assisted reproduction treatment (Youssef et al., 2011). The authors searched electronic databases including MEDLINE, EMBASE and the Cochrane Library, proceedings of major reproductive medicine conferences and reference lists of retrieved articles, until April 2010. Eligible reports were randomized trials comparing GnRH agonist and GnRH antagonist protocols in women undergoing IVF or ICSI cycles. The primary outcome was live birth. Secondary outcomes included OPR and OHSS rates. The meta-analysis calculated summary average rates, rate differences and rate ratios in the individual trials using the inverse variance procedure, or in case of heterogeneity, random effects models (Deeks et al., 2001).
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".