Role of Electrical Stimulation in Peripheral Nerve Regeneration: A Systematic Review
Bibliographic record
Abstract
Functional recovery after peripheral nerve injury is often suboptimal despite the intrinsic permissive growth environment of the peripheral nervous system. The objective of this systematic review is to explore the use of electrical stimulation (ES) for peripheral nerve regeneration. Methods: A systematic literature search was conducted from inception to March 2, 2021 to retrieve articles on ES for peripheral nerve regeneration using the PubMed, Ovid MEDLINE, and Embase databases. Primary outcome measures included objective measures of motor and sensory nerve function. Results: Four randomized control trials, two case reports, and three case series that addressed the aims were identified. The stimulation parameters varied greatly between studies, without an apparent commonality for a given electrical conduit. Outcomes measured included motor (n = 8) and sensory (n = 7) modalities (cold detection, static two-point discrimination, tactile discrimination, and pressure detection), nerve-specific muscle function and bulk, and electromyography (EMG) motor and sensory terminal latency. Different parameters for measurement were utilized and improvement was observed across the studies compared with controls (n = 4) or pre-intervention measurements (n = 5). One randomized control trial reported no benefit of ES and attributed their findings to their stimulation protocol. Complications were documented in three patients only and included wire remnant removal, skin pigmentation, and bone formation. Conclusions: ES in peripheral nerve regeneration is beneficial in improving and accelerating recovery. A meta-analysis was not performed due to the heterogeneity, but all studies showed positive findings and minor to no complications. These results provide a primer for further development of delivery methods.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".