Clinical Diagnosis and Early Medical Management for Endometriosis: Consensus from Asian Expert Group
Bibliographic record
Abstract
This work provides consensus guidance regarding clinical diagnosis and early medical management of endometriosis within Asia. Clinicians with expertise in endometriosis critically evaluated available evidence on clinical diagnosis and early medical management and their applicability to current clinical practices. Clinical diagnosis should focus on symptom recognition, which can be presumed to be endometriosis without laparoscopic confirmation. Transvaginal sonography can be appropriate for diagnosing pelvic endometriosis in select patients. For early empiric treatment, management of women with clinical presentation suggestive of endometriosis should be individualized and consider presentation and therapeutic need. Medical treatment is recommended to reduce endometriosis-associated pelvic pain for patients with no immediate pregnancy desires. Hormonal treatment can be considered for pelvic pain with a clinical endometriosis diagnosis; progestins are a first-line management option for early medical treatment, with oral progestin-based therapies generally a better option compared with combined oral contraceptives because of their safety profile. Dienogest can be used long-term if needed and a larger evidence base supports dienogest use compared with gonadotropin-releasing hormone agonists (GnRHa) as first-line medical therapy. GnRHa may be considered for first-line therapy in some specific situations or as short-term therapy before dienogest and non-steroidal anti-inflammatory drugs as add-on therapy for endometriosis-associated pelvic pain.
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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".