Regenerative Peripheral Nerve Interface: Surgical Protocol for a Randomized Controlled Trial in Postamputation Pain
Bibliographic record
Abstract
Surgical procedures, including nerve reconstruction and end-organ muscle reinnervation, have become more prominent in the prosthetic field over the past decade. Primarily developed to increase the functionality of prosthetic limbs, these surgical procedures have also been found to reduce postamputation neuropathic pain. Today, some of these procedures are performed more frequently for the management and prevention of postamputation pain than for prosthetic fitting, indicating a significant need for effective solutions to postamputation pain. One notable emerging procedure in this context is the Regenerative Peripheral Nerve Interface (RPNI). RPNI surgery involves an operative approach that entails splitting the nerve end longitudinally into its main fascicles and implanting these fascicles within free denervated and devascularized muscle grafts. The RPNI procedure takes a proactive stance in addressing freshly cut nerve endings, facilitating painful neuroma prevention and treatment by enabling the nerve to regenerate and innervate an end organ, i.e., the free muscle graft. Retrospective studies have shown RPNI's effectiveness in alleviating postamputation pain and preventing the formation of painful neuromas. The increasing frequency of utilization of this approach has also given rise to variations in the technique. This article aims to provide a step-by-step description of the RPNI procedure, which will serve as the standardized procedure employed in an international, randomized controlled trial (ClinicalTrials.gov, NCT05009394). In this trial, RPNI is compared to two other surgical procedures for postamputation pain management, specifically, Targeted Muscle Reinnervation (TMR) and neuroma excision coupled with intra-muscular transposition and burying.
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.067 | 0.007 |
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".