Targeted Muscle Reinnervation and Regenerative Peripheral Nerve Interfaces Versus Standard Management in the Treatment of Limb Amputation: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: Painful neuromas are a common postoperative complication of limb amputation often treated with secondary reinnervation. Surgical reinnervation include Targeted Muscle Reinnervation (TMR) and Regenerative Peripheral Nerve Interface (RPNI), and can be primary and secondary. The aim of this review is to assess the effects of primary TMR/RPNI at the time of limb amputation on the incidence and intensity of post-operative neuroma and pain. Methods: This review was registered a priori on PROSPERO (CRD42021264360). A search of the following databases was performed in June 2021: Medline, EMBASE, and CENTRAL. Unpublished trials were searched using clinicaltrials.gov. All randomized and non-randomized studies assessing amputation with a reinnervation strategy (TMR, RPNI) were included. Outcomes evaluated included the incidences of painful neuroma, phantom limb pain (PLP), residual limb pain (RLP), as well as severity of pain, and Pain intensity, behavior, and interference (PROMIS). Results: Eleven studies were included in this systematic review, and five observational studies for quantitative synthesis. Observational study evidence suggests that TMR/RPNI results in a statistically significant reduction in incidence, pain scores and PROMIS scores of PLP and RLP. Decreased incidence of neuromas favored primary TMR/RPNI, but this did not achieve statistical significance (p = 0.07). Included studies had moderate to critical risk of bias. Conclusion: The observational data suggests that primary TMR/RPNI reduces incidence, pain scores and PROMIS scores of PLP and RLP. Going forward, randomized trials are warranted to evaluate this research question, particularly to improve the certainty 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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.030 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".