Conditioning Electrical Stimulation Accelerates Regeneration in Nerve Transfers
Bibliographic record
Abstract
OBJECTIVE: Compared to the upper limb, lower limb distal nerve transfer (DNT) outcomes are poor, likely due to the longer length of regeneration required. DNT surgery to treat foot drop entails rerouting a tibial nerve branch to the denervated common fibular nerve stump to reinnervate the tibialis anterior muscle for ankle dorsiflexion. Conditioning electrical stimulation (CES) prior to nerve repair surgery accelerates nerve regeneration and promotes sensorimotor recovery. We hypothesize that CES prior to DNT will promote nerve regeneration to restore ankle dorsiflexion. METHODS: One week following common fibular nerve crush, CES was delivered to the tibial nerve in half the animals, and at 2 weeks, all animals received a DNT. To investigate the effects of CES on nerve regeneration, a series of kinetic, kinematic, skilled locomotion, electrophysiologic, and immunohistochemical outcomes were assessed. The effects of CES on the nerve were investigated. RESULTS: CES-treated animals had significantly accelerated nerve regeneration (p < 0.001), increased walking speed, and improved skilled locomotion. The injured limb had greater vertical peak forces, with improved duty factor, near-complete recovery of braking, propulsive forces, and dorsiflexion (p < 0.01). Reinnervation of the tibialis anterior muscle was confirmed with nerve conduction studies and immunohistochemical analysis of the neuromuscular junction. Immunohistochemistry demonstrated that CES does not induce Wallerian degeneration, nor does it cause macrophage infiltration of the distal tibial nerve. INTERPRETATION: Tibial nerve CES prior to DNT significantly improved functional recovery of the common fibular nerve and its muscle targets without inducing injury to the donor nerve. ANN NEUROL 2020;88:363-374.
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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) | 0.003 | 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".