OC09.02: The influence of severe endometriosis on the accuracy of transvaginal ultrasound diagnosis of uterosacral ligament endometriosis
Bibliographic record
Abstract
The uterosacral ligaments (USLs) and torus uterinus (TU) are common sites for deep endometriosis (DE) but are challenging to diagnose through transvaginal ultrasound (TVS). Anatomical distortion caused by severe endometriosis, including pouch of Douglas (POD) obliteration and bowel DE, may contribute to poor diagnostic performance. This study aims to assess the impact of severe endometriosis and anatomic distortion on TVS diagnosis of DE in the USLs and TU. We performed a secondary analysis on data from a prospective diagnostic test accuracy study conducted at the McMaster University Medical Center, Tertiary Endometriosis Clinic, using consecutively recruited participants. The index test was TVS performed by an advanced sonologist. Diagnostic accuracy parameters were calculated for each site relative to the reference standard at 1) baseline, 2) excluding those with POD obliteration, and 3) excluding DE of the bowel. Fifty-four consecutive participants were included. Baseline diagnostic performance for the left USL, right USL, and TU without exclusion was: accuracy 92.6%, 94.4%, 100%, sensitivity 82.6%, 75.0%, 100%, and specificity 100%, 100%, 100%, respectively. Upon excluding those with POD obliteration, the diagnostic performance was: accuracy 90.2%, 97.6%, 100%, sensitivity 73.3%, 85.7%, 100%, and specificity 100%, 100%, 100%, respectively. Comparatively, diagnostic performance excluding those with DE of the bowel was: accuracy 93.2%, 95.5%, 100.0%, sensitivity 82.4%, 71.4%, 100%, and specificity 100%, 100%, 100%, respectively. Despite the challenges posed by severe endometriosis, which distorts the anatomical environment with extensive adhesions, potentially making it difficult to map normal and diseased tissue, our results demonstrate strong diagnostic performance regardless of disease complexity. Our findings show robustness in diagnosing and detecting DE through TVS.
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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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".