Targeted Muscle Reinnervation: Considerations for Future Implementation in Adolescents and Younger Children
Bibliographic record
Abstract
Prosthetic options for patients with proximal upper limb absence are limited. Current above-elbow prostheses may restore basic motor functions for crucial activities, but they are cumbersome to operate, lack sensory feedback, and are often abandoned. Targeted muscle reinnervation is a novel surgical procedure that enhances the ability of patients with above-elbow amputations to intuitively control a myoelectric prosthesis. By transferring multiple severed peripheral nerves to a robust target muscle, targeted muscle reinnervation restores physiologic continuity and enables more intuitive prosthetic control. Although reports have been limited to adults, targeted muscle reinnervation has great potential for application in a pediatric population with congenital or acquired proximal upper limb absence. In this review, the authors describe the rehabilitative challenges of proximal upper limb amputees and outline the objectives, techniques, and outcomes of targeted muscle reinnervation. The authors then discuss important considerations for adapting targeted muscle reinnervation to pediatric patients, including cause of upper limb absence, central plasticity, timing of prosthesis fitting, role of the family, surgical feasibility, and bioethical aspects. The authors believe that carefully screened school-age children and adolescents with bilateral proximal upper limb absence, and select adolescents with unilateral proximal upper limb absence, should be seriously considered for targeted muscle reinnervation performed by an experienced surgical and rehabilitation team.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".