Electrical stimulation enhances sensory recovery: A randomized controlled trial
Bibliographic record
Abstract
OBJECTIVE: Brief postsurgical electrical stimulation (ES) has been shown to enhance peripheral nerve regeneration in animal models following axotomy and crush injury. However, whether this treatment is beneficial in humans with sensory nerve injury has not been tested. The goal of this study was to test the hypothesis that ES would enhance sensory nerve regeneration following digital nerve transection compared to surgery alone. METHODS: Patients with complete digital nerve transection underwent epineurial nerve repair. After coaptation of the severed nerve ends, fine wire electrodes were implanted before skin closure. Postoperatively, patients were randomized to receiving either 1 hour of 20Hz continuous ES or sham stimulation in a double-blinded manner. Patients were followed monthly for 6 months by a blinded evaluator to monitor physiological recovery of spatial discrimination, pressure threshold, and quantitative small fiber sensory testing. Functional disability was measured using the Disability of Arm, Shoulder, and Hand questionnaire. RESULTS: A total of 36 patients were recruited, with 18 in each group. Those in the ES group showed consistently greater improvements in all sensory modalities by 5 to 6 months postoperatively compared to the controls. Although there was a trend of greater functional improvements in the ES group, it was not statistically significant (p > 0.01). INTERPRETATION: Postsurgical ES enhanced sensory reinnervation in patients who sustained complete digital nerve transection. The conferred benefits apply to a wide range of sensory functions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".