Are sonographers the future ‘gold standard’ in the diagnosis of endometriosis?
Bibliographic record
Abstract
Abstract Diagnosis of endometriosis has traditionally relied on laparoscopic surgery, which was considered the ‘gold standard’ diagnostic tool. This is not ideal as surgery carries risk, is expensive, is difficult to access, and disrupts patients work or education due to the recovery time needed. As such, imaging has been investigated as a potential method for non‐invasive diagnosis, with transvaginal ultrasound showing high diagnostic accuracy for ovarian endometriomas and deep endometriosis. The advances in imaging capability led to recent international guidelines suggesting laparoscopy is no longer the ‘gold‐standard’ for diagnosis and encouraging clinicians to utilise medical imaging as part of their diagnostic work‐up for endometriosis. Imaging is emerging as not only a tool for planning endometriosis surgery but increasingly as the first approach for initial diagnosis. Given that transvaginal ultrasound is the primary imaging modality for assessment of gynaecological conditions, it is inevitable that sonographers will have a significant future role in endometriosis diagnosis. This moves away from endometriosis diagnosis being the exclusive realm of laparoscopic surgeons and increasingly involves medical imaging specialists. This review article will describe the origins of endometriosis ultrasound and the current capabilities of transvaginal ultrasound in this field. The expectations of sonographers in this evolving space will be explored, as well as recent novel research findings to gain insight into what the future of endometriosis diagnosis with ultrasound may look like.
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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 | 0.023 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".