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 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 | 0.002 | 0.085 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".