A Direct Comparison of Targeted Muscle Reinnervation and Regenerative Peripheral Nerve Interfaces to Prevent Neuroma Pain
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Targeted muscle reinnervation (TMR) and regenerative peripheral nerve interface (RPNI) surgeries manage neuroma pain; however, there remains considerable discord regarding the best treatment strategy. We provide a direct comparison of TMR and RPNI surgery using a rodent model for the treatment of neuroma pain. METHODS: The tibial nerve of 36 Fischer rats was transected and secured to the dermis to promote neuroma formation. Pain was assessed using mechanical stimulation at the neuroma site (direct pain) and von Frey analysis at the footpad (to assess tactile allodynia from collateral innervation). Once painful neuromas were detected 6 weeks later, animals were randomized to experimental groups: (a) TMR to the motor branch to biceps femoris, (b) RPNI with an extensor digitorum longus graft, (c) neuroma excision, and (d) neuroma in situ. The TMR/RPNIs were harvested to confirm muscle reinnervation, and the sensory ganglia and nerves were harvested to assess markers of regeneration, pain, and inflammation. RESULTS: Ten weeks post-TMR/RPNI surgery, animals had decreased pain scores compared with controls ( P < .001) and they both demonstrated neuromuscular junction reinnervation. Compared with neuroma controls, immunohistochemistry showed that sensory neuronal cell bodies of TMR and RPNI showed a decrease in regeneration markers phosphorylated cyclic AMP receptor binding protein and activation transcription factor 3 and pain markers transient receptor potential vanilloid 1 and neuropeptide Y ( P < .05). The nerve and dorsal root ganglion maintained elevated Iba-1 expression in all cohorts. CONCLUSION: RPNI and TMR improved pain scores after neuroma resection suggesting both may be clinically feasible techniques for improving outcomes for patients with nerve injuries or those undergoing amputation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".