Laparoscopic approach to refractory extraspinal sciatica and pudendal pain caused by intrapelvic nerve entrapment
Bibliographic record
Abstract
Entrapments of the intrapelvic portions of the lumbosacral plexus are an important extraspinal cause of sciatica and pudendal neuralgia. They can be treated using Laparoscopic Neuronavigation (LANN), a minimally invasive technique that has set the foundations of an emerging field in Medicine-Neuropelveology. This retrospective-prospective study analyzes the outcomes of 63 patients treated with the LANN technique over a 10 year time period. One year after surgery, 78.3% of patients reported clinically relevant pain reduction, defined as ≥ 50% reduction in Numeric Rating Scale (NRS) score; these results were maintained for a mean follow up of 3.2 years. Preoperative chronic opioid use (≥ 4 months of ≥ 10 mg morphine equivalents/day) was a predictor of poor surgical outcome-clinically relevant pain reduction was observed in only 30.8% in this group of patients, compared to 91.5% in patients not regularly taking opioids preoperatively (p < 0.01). Perioperative complication rate was 20%. Our results indicate that the LANN technique is an effective and reproducible approach to relieve pain secondary to intrapelvic nerve entrapments and that preoperative chronic opioid therapy significantly reduces the likelihood of a successful surgical outcome. This study provides detailed information on perioperative complication and postoperative course, which is essential for patient consenting.
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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 | 0.001 |
| 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) | 0.001 | 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".