Delayed peripheral nerve repair: methods, including surgical ′cross-bridging′ to promote nerve regeneration
Bibliographic record
Abstract
Despite the capacity of Schwann cells to support peripheral nerve regeneration, functional recovery after nerve injuries is frequently poor, especially for proximal injuries that require regenerating axons to grow over long distances to reinnervate distal targets. Nerve transfers, where small fascicles from an adjacent intact nerve are coapted to the nerve stump of a nearby denervated muscle, allow for functional return but at the expense of reduced numbers of innervating nerves. A 1-hour period of 20 Hz electrical nerve stimulation via electrodes proximal to an injury site accelerates axon outgrowth to hasten target reinnervation in rats and humans, even after delayed surgery. A novel strategy of enticing donor axons from an otherwise intact nerve to grow through small nerve grafts (cross-bridges) into a denervated nerve stump, promotes improved axon regeneration after delayed nerve repair. The efficacy of this technique has been demonstrated in a rat model and is now in clinical use in patients undergoing cross-face nerve grafting for facial paralysis. In conclusion, brief electrical stimulation, combined with the surgical technique of promoting the regeneration of some donor axons to 'protect' chronically denervated Schwann cells, improves nerve regeneration and, in turn, functional outcomes in the management of peripheral nerve injuries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.002 | 0.002 |
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".