Comparison of 2 Regenerative Peripheral Nerve Interface Techniques for the Treatment of Rat Neuroma Pain
Bibliographic record
Abstract
SUMMARY: Treatment of painful neuromas has long posed a significant challenge for peripheral nerve patients. The regenerative peripheral nerve interface (RPNI) provides the transected nerve with a muscle graft target to prevent neuroma formation. Discrepancies in RPNI surgical techniques between animal models ("inlay" RPNI) and clinical studies ("burrito" RPNI) preclude direct translation of results from bench to bedside and may account for variabilities in patient outcomes. The authors compared outcomes of these 2 surgical techniques in a rodent model. Animals treated with burrito RPNI after tibial nerve neuroma formation demonstrated no improvement in pain assessment, and tissue analysis revealed complete atrophy of the muscle graft with neuroma recurrence. By contrast, animals treated with inlay RPNI had significant improvement in pain with viable muscle grafts. The results suggest superiority of the inlay RPNI surgical technique for the management of painful neuroma in rodents. CLINICAL RELEVANCE STATEMENT: RPNIs are currently being used to prevent and treat neuroma and phantom limb pain. This preclinical study suggests the superiority of one surgical technique over the other.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".