Epineural Dissection Is a Safe Technique That Facilitates Limb Salvage Surgery
Bibliographic record
Abstract
UNLABELLED: Epineural dissection has been used in our center for the past 19 years as a means of preserving the sciatic nerve when it is closely applied to a soft tissue sarcoma. Our aim in doing this study was to establish if this technique resulted in increased local or systemic recurrence of the tumor. In addition, we assessed functional outcomes. Forty-three patients had an epineural dissection done during primary resection of a malignant thigh tumor. These patients were compared with 44 patients with tumors that were of similar size and grade but distant from the nerve. We also analyzed seven patients who required nerve resection. There was no difference in local or systemic recurrence rates or functional outcomes when epineural dissection was done. Those with nerve resection had worse Musculoskeletal Tumor Society scores but equivalent Toronto Extremity Salvage Scores to those with an epineural dissection. We conclude that epineural dissection (when combined with radiotherapy in a planned multidisciplinary approach to limb salvage) is both a safe and effective procedure to preserve the sciatic nerve and that nerve resection should be limited to situations where the nerve is completely encased in tumor. LEVEL OF EVIDENCE: Prognostic study, Level II-2 (retrospective cohort study). See the Guidelines for Authors for a complete description of levels of evidence.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".