EP24.05: Prospective study: transvaginal ultrasound for endometriosis, incorporating IDEA consensus and novel superficial endometriosis inclusion
Bibliographic record
Abstract
Transvaginal ultrasound (TVS) is crucial for diagnosing endometriosis, guided by the International Deep Endometriosis Analysis (IDEA) guidelines. While TVS excels in diagnosing ovarian (OE) and deep endometriosis (DE), superficial endometriosis (SE) remains elusive. Previous studies relied on outdated classification guidelines, hindering our understanding of TVS diagnostic accuracy. This study aims to pioneer our understanding of TVS accuracy for DE, OE, and SE, using IDEA consensus and updated guidelines. We performed a prospective diagnostic accuracy study at a tertiary endometriosis referral centre. The index test was TVS, reported in accordance with IDEA consensus and laparoscopy as the reference standard, conducted by the same surgeon sonologist. SE and DE were defined according to the novel classification guidelines as the presence or absence of infiltration. Accuracy (Acc), sensitivity (Sens), specificity (Spec), positive and negative predictive values (PPV/NPV) were calculated for each phenotype and site relative to the reference standard. 125 participants were included. Diagnostic performance of TVS in DE: Acc 92.7 - 99.2%, Sens 82.3 - 100%, Spec 97.0 - 100%, PPV 94.1 - 100%, NPV 88.9 - 100%. For OE of the left and right ovary, respectively, was: Acc 98.3% and 98.4%, Sens 95.0% and 94.9%, Spec 100% and 100%, PPV 100% and 100%, NPV 97.6% and 97.7%. SE assessment in the population excluding DE, OE, and POD obliteration showed: Acc 73.9 - 87.0%, Sens 12.5 - 54.9%, Spec 100%, PPV 100%, NPV 72.2 - 84.6%. TVS demonstrates strong diagnostic performance for DE and OE across anatomical sites. While diagnosing SE is challenging, TVS exhibits strong specificity. These findings support TVS as a primary diagnostic for DE and OE, with enhanced accuracy. However, novel diagnostics are needed to confidently rule out SE, reducing diagnostic delay and improving surgical planning.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".