Motor Neuron Regeneration through End-to-Side Repairs Is a Function of Donor Nerve Axotomy
Bibliographic record
Abstract
BACKGROUND: Over the past decade, a growing body of literature has emerged supporting the use of end-to-side (terminolateral) neurorrhaphy for the treatment of selected peripheral nerve injuries. It remains unclear, however, whether injury to the donor nerve is necessary to achieve significant regeneration through such repairs. METHODS: End-to-side repair was studied in a rodent model in which the terminal limb of a transected peroneal nerve was sutured to the lateral aspect of the tibial nerve. Twenty-eight Lewis rats were randomized to four groups of seven animals each corresponding to incrementally greater donor nerve injuries as follows: group 1, conventional end-to-side neurorrhaphy; group 2, end-to-side neurorrhaphy with proximal crush injury; group 3, end-to-side neurorrhaphy with neurotomy; and group 4, end-to-end repair of transected peroneal nerve (positive control). RESULTS: At 12 weeks, retrograde labeling of cell bodies of the ventral horn demonstrated significant differences between experimental groups, with mean counts in group 4 (1237 +/- 171) > group 3 (522 +/- 204) > group 2 (210 +/- 132) > or = group 1 (126 +/- 146). This association between nerve injury and motor neuron counts was closely mirrored in quantitative assessments of peripheral nerve regeneration and normalized wet muscle masses. CONCLUSIONS: These data support the hypothesis that donor nerve injury is a prerequisite for significant motor neuronal regeneration across end-to-side repairs. Motor neuron regeneration through end-to-side repairs is optimized by deliberate transection of donor nerve axons.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".