Oral Gonadotropin-Releasing Hormone Antagonists in the Treatment of Endometriosis: Advances in Research
Bibliographic record
Abstract
Non-peptide gonadotropin-releasing hormone (GnRH) receptor antagonists exhibit remarkable potency and specificity in inhibiting GnRH receptor activity. The orally administered versions of these drugs, notably elagolix and relugolix, have obtained official clearance in various countries for treating moderate-to-severe endometriosis-related pain. Concurrently, linzagolix and opigolix (ASP1707) continue to advance through late-stage clinical trials. The primary objective of this review is to comprehensively evaluate the clinical efficacy and safety profile of oral GnRH antagonists, specifically elagolix, relugolix, linzagolix, and opigolix, for the management of endometriosis-associated pain. Specifically, this study summarizes and analyzes their effectiveness in alleviating dysmenorrhea and non-menstrual pelvic pain, evaluates the dose-dependent impacts on bone mineral density and adverse effects such as hot flushes, and explores the role of add-back therapy in improving treatment safety and patient adherence. Research has demonstrated that oral GnRH antagonists effectively alleviate endometriosis-related pain while enhancing patients' quality of life. Furthermore, when combined with add-back therapy, these medications enhance treatment safety and contribute to greater patient compliance. Compared to alternative hormonal treatments, oral GnRH antagonists emerge as a particularly promising approach for managing endometriosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".