Conditioning electrical nerve stimulation enhances functional rewiring in a mouse model of nerve transfer to treat chronic spinal cord injury
Bibliographic record
Abstract
Background/Objectives: Nerve transfer surgery is a state-of-the-art surgical approach to restore hand and arm function in individuals living with tetraplegia, significantly im-pacting daily life. While nearly a third of all individuals with chronic spinal cord injury may benefit from this intervention, variability in outcomes can limit the functional im-pact. A bedside to bench approach was taken to address the variable response of tetra-plegic individuals to nerve transfer surgery. Methods: We used a hierarchical multiple factor analysis to evaluate the effects of conditioning electrical stimulation (CES) on out-comes in a mouse model of nerve transfer to treat chronic cervical spinal cord injury. Results: We found that CES of donor nerves one week prior to nerve transfer surgery enhanced anatomical and functional measures of innervation of targeted muscles. Fur-thermore, CES increased the rate of recovery of naturalistic behavior. Conclusions: While the model has some limitations due to the small size of the rodent, our results support the use of CES as an effective approach to improve outcomes in clinical nerve repair 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.012 |
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".