Regenerative Peripheral Nerve Interface Surgery to Treat Chronic Postamputation Pain: A Prospective Study in Major Lower Limb Amputation Patients
Bibliographic record
Abstract
Objective: The objective was to assess the postsurgical outcomes of regenerative peripheral nerve interface (RPNI) surgery in a prospective cohort of major lower extremity amputation patients with chronic postamputation pain. Background: Chronic pain in lower limb amputation patients is commonly the result of neuroma formation after traumatic peripheral nerve injury. By implanting more proximal transected nerve ends into autologous free muscle grafts, RPNI surgery can treat postamputation pain by diminishing the development of neuromas. RPNI surgery in prior retrospective studies has been shown to mitigate postamputation pain. Methods: Twenty-two lower limb amputation patients with established chronic postamputation pain were recruited from 2 studies in this prospective study. All patients underwent RPNI surgery to treat identified symptomatic neuromas within the residual limb. Patient-reported outcome instruments were administered preoperatively and postoperatively at 1 week, 4 months, and 12 months to examine residual limb pain (McGill Pain Questionnaire, PROMIS Pain Intensity, and PROMIS Pain Interference), phantom limb pain (modified PROMIS Pain Intensity and Phantom Limb sensation questionnaire), psychosocial status (PHQ-9, GAD-7, and PCS), and functional (OPUS) outcomes. Results: RPNI surgery significantly improved residual limb pain. While phantom limb sensation improved significantly, phantom limb pain demonstrated a modest decrease. Psychosocial outcomes also improved significantly after RPNI surgery. Prosthetic use slightly increased, and patients did not experience loss of function. Conclusions: RPNI surgery leverages the processes of reinnervation to successfully treat residual limb pain and improve psychosocial outcomes in patients with chronic postamputation pain. Phantom limb pain may be more difficult to treat in chronic pain patients who have central sensitization at the time of surgery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".