Pain and Functional Outcomes following Targeted Muscle Reinnervation: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: It is estimated that by 2050, a total of 3.6 million patients will be living with an amputation in the United States. The objective of this systematic review is to evaluate the effect of targeted muscle reinnervation (TMR) on pain and physical functioning in amputees. METHODS: A literature search was performed on PubMed, Embase, and MEDLINE up to November 28, 2021. Clinical studies assessing the outcomes of TMR (pain, prosthesis control, life quality, limb function, and disability) were included. RESULTS: Thirty-nine articles were included. The total number of patients who underwent TMR was 449, and 716 were controls. Mean follow-up was 25 months. A total of 309 (66%) lower-limb and 159 (34%) upper-limb amputations took place in the TMR group, the most common being below-knee amputations (39%). The control group included a total of 557 (84%) lower-limb and 108 (16%) upper-limb amputations; the greatest proportion being below-knee amputations in this group as well (54%). Trauma was the most common indication for amputation. Phantom limb pain scores were lower by 10.2 points for intensity ( P = 0.01), 4.67 points for behavior ( P = 0.01), and 8.9 points for interference ( P = 0.09). Similarly, residual limb pain measures were lower for cases for intensity, behavior, and interference, but they failed to reach significance. Neuroma symptoms occurred less frequently, and functional and prosthesis control outcomes improved following TMR. CONCLUSION: The literature evidence suggests that TMR is a promising therapy for improving pain, prosthesis use, and functional outcomes after limb 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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".