A Long Peripheral Nerve Autograft Model in the Sheep Forelimb
Bibliographic record
Abstract
BACKGROUND: Autologous nerve grafts remain the only proven means of bridging lengthy gaps in peripheral nerve. However, there is very little literature on a reliable long (> 5 cm) nerve autograft animal model. OBJECTIVE: To establish a reproducible long nerve gap and autograft animal model that is clinically relevant but not cost prohibitive. METHODS: The extent of nerve regeneration and electrophysiological recovery after segmental repair of a long nerve defect was evaluated with a sheep model. Thirteen Suffolk sheep were used. An 18-cm segment of radial sensory nerve was harvested from the forelimb, trimmed, divided into 2 equal segments of 7 cm each, and microsurgically repaired to a surgically created defect of 5 cm in the median nerve within the same forelimb. Electrophysiological studies were performed on 6 sheep at 6 months and 6 sheep at 9 months. Samples of the grafted segments were obtained for histology, immunohistochemistry, and morphometric analyses. Electric studies were also performed on an uninjured median nerve of a control animal in tissue that was similarly harvested and processed. RESULTS: At 6 and 9 months, all sheep had recordable robust nerve action potentials. Nerve conduction velocity and amplitude were slightly decreased compared with control, but the difference was statistically insignificant. Histomorphometric assessment demonstrated that the autografts contained a large number of regenerating axons through graft fascicles in all animals. CONCLUSION: The median nerve in the sheep forelimb is a reproducible and reliable model for assessing regeneration through long peripheral nerve grafts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".