Non-Invasive Imaging Techniques for Diagnosis of Pelvic Deep Endometriosis and Endometriosis Classification Systems: An International Consensus Statement
Bibliographic record
Abstract
The International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) and International Deep Endometriosis Analysis (IDEA) group, the European Endometriosis League (EEL), the European Society for Gynaecological Endoscopy (ESGE), the European Society of Human Reproduction and Embryology (ESHRE), the International Society for Gynecologic Endoscopy (ISGE), the American Association of Gynecologic Laparoscopists (AAGL) and the European Society of Urogenital Radiology (ESUR) elected an international, multidisciplinary panel of gynecological surgeons, sonographers and radiologists, including a steering committee, which searched the literature for relevant articles in order to review the literature and provide evidence-based and clinically relevant statements on the use of imaging techniques for non-invasive diagnosis and classification of pelvic deep endometriosis. Preliminary statements were drafted based on review of the relevant literature. Following two rounds of revisions and voting orchestrated by chairs of the participating societies, consensus statements were finalized. A final version of the document was then resubmitted to the society chairs for approval. Twenty statements were drafted, of which 14 reached strong and three moderate agreement after the first voting round. The remaining three statements were discussed by all members of the steering committee and society chairs and rephrased, followed by an additional round of voting. At the conclusion of the process, 14 statements had strong and five statements moderate agreement, with one statement left in equipoise. This consensus work aims to guide clinicians involved in treating women with suspected endometriosis during patient assessment, counseling and planning of surgical treatment strategies.
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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 | 0.143 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.014 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".