Endovaginal ultrasound-assisted pain mapping in endometriosis and chronic pelvic pain
Bibliographic record
Abstract
The objective of this study was to determine if the combination of tenderness-guided endovaginal ultrasound and digital pelvic exam (i.e. EVUS-assisted exam) for preoperative pain mapping, in cases without nodules or endometriomas, increases sensitivity/specificity for laparoscopic findings. This was a retrospective review of women with chronic pelvic pain ± infertility with preoperative pain mapping exam prior to laparoscopy (n = 97, 2006-7). Predictor variables (EVUS-assisted exam vs digital pelvic exam alone, for pain mapping) were coded as tender vs non-tender. Primary outcome was findings on laparoscopy (e.g. endometriosis or adhesions) and was coded as abnormal vs normal. We found that EVUS-assisted exam had greater sensitivity (0.81, 95% CI: 0.70-0.89) for abnormal laparoscopy compared with digital pelvic exam alone (0.58, 95% CI: 0.46-0.69) (McNemar's test, p < 0.001). Specificity was limited for both types of pain mapping (0.22, 95% CI: 0.08-0.44 for EVUS-assisted; and 0.39, 95% CI: 0.20-0.61 for digital), with no significant difference (p = 0.13). In conclusion, in the absence of nodules or endometriomas, EVUS-assisted exam increases sensitivity, but with no benefit in specificity, for prediction of abnormal laparoscopy.
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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 | 0.018 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".