Diagnostic accuracy of transvaginal ultrasound for detection of endometriosis using International Deep Endometriosis Analysis (<scp>IDEA</scp>) approach: prospective international pilot study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the diagnostic accuracy of transvaginal ultrasound (TVS) in predicting deep endometriosis (DE) following the International Deep Endometriosis Analysis (IDEA) consensus methodology. METHODS: This was an international multicenter prospective diagnostic accuracy study involving eight centers across six countries (August 2018-November 2019). Consecutive participants with endometriosis suspected based on clinical symptoms or historical diagnosis of endometriosis were included. The index test was TVS performed preoperatively in accordance with the IDEA consensus statement. At each center, the index test was interpreted by a single sonologist. Reference standards were: (1) direct visualization of endometriosis at laparoscopy, as determined by a non-blinded surgeon with expertise in endometriosis surgery; and (2) histological assessment of biopsied/excised tissue. Surgery was performed within 12 months following the index TVS. Accuracy, sensitivity, specificity, positive and negative predictive values (PPV and NPV) and positive and negative likelihood ratios (LR+ and LR-) of TVS in the diagnosis of DE were calculated. RESULTS: Included in the study were 273 participants with complete clinical, TVS, laparoscopic and histological data. Of these, based on histology, 256 (93.8%) were confirmed to have endometriosis, including superficial endometriosis, and 190 (69.6%) were confirmed to have DE. Based on surgical visualization, 207/273 (75.8%) patients had DE. For DE overall, the diagnostic performance of TVS based on surgical visualization as the reference standard was as follows: accuracy, 86.1%; sensitivity, 88.4%; specificity, 78.8%; PPV, 92.9%; NPV, 68.4%; LR+, 4.17; LR-, 0.15, and the diagnostic performance of TVS based on histology as the reference standard was as follows: accuracy, 85.9%; sensitivity, 89.8%; specificity, 75.9%; PPV, 90.4%; NPV, 74.6%; LR+, 3.72; LR-, 0.13. CONCLUSIONS: Using the IDEA consensus methodology provides strong diagnostic accuracy for TVS assessment of DE. We found a higher TVS detection rate of DE overall than that reported by the most recent meta-analysis on the topic (sensitivity, 79%), albeit with a lower specificity. © 2022 International Society of Ultrasound in Obstetrics and Gynecology.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".