Influence of Complex Disease on the Accuracy of Transvaginal Ultrasound Diagnosis of Uterosacral Ligament Endometriosis
Bibliographic record
Abstract
Objective: To assess the impact of complex disease states, including pouch of Douglas (POD) obliteration and deep endometriosis (DE) of the bowel, on the diagnostic accuracy of transvaginal ultrasound (TVUS) for detecting endometriosis of the uterosacral ligaments (USLs) and torus uterinus (TU). Methods: This diagnostic accuracy study evaluated the performance of TVUS in diagnosing DE of the USLs and TU, using laparoscopic visualisation with histological confirmation as the reference standard among two previously reported prospectively collected cohorts. Complex disease states were defined as complete POD obliteration and/or DE of the bowel. Diagnostic accuracy metrics, including sensitivity, specificity, positive and negative predictive values (PPV) and likelihood ratios (LR), were calculated before and after the exclusion of complex disease cases. Results: Among 177 participants, 18.6% (33/177) had POD obliteration, 18.6% (33/177) had DE of the bowel and 16.4% (29/177) had both. Accuracy ranged from 93.1% to 94.7% for USLs and 97.2%-98.5% for TU, with minimal change after exclusion (≤ 1.5%). Sensitivity declined following exclusion, by -6.4% (left USL), -3.5% (right USL) and -2.7% (TU) after POD obliteration exclusion and further decreases of -1.8%, -3.4% and -4.4%, respectively, after bowel DE exclusion. Specificity remained ≥ 97.8% across all sites and reached 100% at the USLs after POD obliteration exclusion. Conclusions: Contrary to the assumption that complex disease states hinder TVUS accuracy, their presence may enhance lesion recognition, likely due to increased sonographic attentiveness when severe disease is suspected. While TVUS remains highly specific, its sensitivity decreases in the absence of complex disease, emphasising the need for meticulous and systematic imaging approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".