Electrical Stimulation Therapy to Accelerate Nerve Regeneration Remains Effective Following Postoperative Application of Lidocaine
Bibliographic record
Abstract
Background: Brief electrical stimulation (ES) of injured peripheral nerves for 1 hour has been shown to accelerate nerve regeneration with proximal action potential conduction to the neuron cell body, a requirement to elicit therapeutic benefit. Local anesthetic is often used to manage pain in patients. However, using lidocaine after ES therapy has been controversial. We assessed the effects of extraneural usage of lidocaine after ES therapy on nerve regeneration in a rodent nerve injury model. Methods: Lewis rats underwent tibial nerve transection and immediate repair and randomized to 4 groups: control (REP), extraneural lidocaine alone (REP + LIDO), 60-minute ES (60 ES), and 60-minute ES with extraneural lidocaine (60 ES + LIDO). The tibial nerve was retrograde labeled distally from the neurorrhaphy 28 days post repair. Spinal cords and dorsal root ganglia were harvested to assess motor and sensory neuron counts. Data were analyzed using 1-way analysis of variance (ANOVA) with a post-hoc Tukey correction. Results: Using lidocaine after nerve repair did not affect nerve regeneration in the control group (REP vs REP + LIDO) or ES group (60 ES vs 60 ES + LIDO), with motor and sensory neuron counts not statistically different between groups. Electrical stimulation therapy showed at least a 60% increase in motor and sensory neuron counts than controls, a statistically significant effect ( P < .001). Conclusions: Extraneural usage of lidocaine after ES does not abolish the improved effect of ES on nerve regeneration. Future clinical studies should evaluate the usage of subcutaneous injection of lidocaine post ES for analgesia control.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".