Conditioning Electrical Stimulation Is Superior to Postoperative Electrical Stimulation in Enhanced Regeneration and Functional Recovery Following Nerve Graft Repair
Bibliographic record
Abstract
Background. Autologous nerve graft is the most common clinical intervention for repairing a nerve gap. However, its regenerative capacity is decreased in part because, unlike a primary repair, the regenerating axons must traverse 2 repair sites. Means to promote nerve regeneration across a graft are needed. Postoperative electrical stimulation (PES) improves nerve growth by reducing staggered regeneration at the coaptation site whereas conditioning electrical stimulation (CES) accelerates axon extension. In this study, we directly compared these electrical stimulation paradigms in a model of nerve autograft repair. Methods. To lay the foundation for clinical translation, regeneration and reinnervation outcomes of CES and PES in a 5-mm nerve autograft model were compared. Sprague-Dawley rats were divided into: ( a) CES, ( b) PES, and ( c) no stimulation cohorts. CES was delivered 1 week prior to nerve cut/coaptation, and PES was delivered immediately following coaptation. Length of nerve regeneration (n = 6/cohort), and behavioral testing (n = 16/cohort) were performed at 14 days and 6 to 14 weeks post-coaptation, respectively. Results. CES treated axons extended 5.9 ± 0.2 mm, significantly longer than PES (3.8 ± 0.2 mm), or no stimulation (2.5 ± 0.2 mm) ( P < .01). Compared with PES animals, the CES animals had significantly improved sensory recovery (von Frey filament testing, intraepidermal nerve fiber reinnervation) ( P < .001) and motor reinnervation (horizontal ladder, gait analysis, nerve conduction studies, neuromuscular junction analysis) ( P < .01). Conclusion. CES resulted in faster regeneration through the nerve graft and improved sensorimotor recovery compared to all other cohorts. It is a promising treatment to improve outcomes in patients undergoing nerve autograft repair.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".