The use of electrical stimulation to enhance recovery following peripheral nerve injury
Bibliographic record
Abstract
Peripheral nerve injury is common and can have devastating consequences. In severe cases, functional recovery is often poor despite surgery. This is primarily due to the exceedingly slow rate of nerve regeneration at only 1-3 mm/day. The local environment in the distal nerve stump supportive of nerve regrowth deteriorates over time and the target end organs become atrophic. To overcome these challenges, investigations into treatments capable of accelerating nerve regrowth are of great clinical relevance and are an active area of research. One intervention that has shown great promise is perioperative electrical stimulation. Postoperative stimulation helps to expedite the Wallerian degeneration process and reduces delays caused by staggered regeneration at the site of nerve injury. By contrast, preoperative "conditioning" stimulation increases the rate of nerve regrowth along the nerve trunk. Over the past two decades, a rich body of literature has emerged that provides molecular insights into the mechanism by which electrical stimulation impacts nerve regeneration. The end result is upregulation of regeneration-associated genes in the neuronal body and accelerated transport to the axon front for regrowth. The efficacy of brief electrical stimulation on patients with peripheral nerve injuries was demonstrated in a number of randomized controlled trials on compressive, transection and traction injuries. As approved equipment to deliver this treatment is becoming available, it may be feasible to deploy this novel treatment in a wide range of clinical settings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".