Laparoscopic Inguinal Exploration and Mesh Placement for Chronic Pelvic Pain
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Chronic pelvic pain affects 15% of women. Our objective was to evaluate empiric laparoscopic inguinal exploration and mesh placement in this population. METHODS: Retrospective cohort with follow-up questionnaire of women with lateralizing chronic pelvic pain (right or left), ipsilateral inguinal tenderness on pelvic examination, no clinical hernia on abdominal examination, and ipsilateral empiric laparoscopic inguinal exploration with mesh placement (2003-2009). Primary outcome was pain level at the last postoperative visit. Secondary outcomes were pain level and SF-36 scores from the follow-up questionnaire. RESULTS: Forty-eight cases met the study criteria. Surgery was done empirically for all patients, with only 7 patients (15%) found to have an ipsilateral patent processus vaginalis (shallow peritoneal dimple or a deeper defect (occult hernia)). Of 43 cases informative for the primary outcome, there was pain improvement in 15 patients (35%); pain improvement then return of the pain in 18 patients (42%); and pain unchanged in 9 patients (21%) and worse in 1 patient (2%). Improvement in pain was associated with a positive Carnett's test in the ipsilateral abdominal lower quadrant (P = .024). Thirteen patients returned the questionnaire (27%), and the pain was now described as improved in 9 patients (69%), unchanged in 4 patients (31%), and worse in none. Three SF-36 subscales showed improvement (physical functioning, social functioning, and pain). CONCLUSION: In select women with chronic pelvic pain, empiric laparoscopic inguinal exploration and mesh placement results in moderate improvement in outcome. A positive Carnett's test in the ipsilateral abdominal lower quadrant is a predictor of better outcome.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".