Perineurial Window is Critical for Experimental Reverse End-to-Side Nerve Transfer
Bibliographic record
Abstract
BACKGROUND: The depth of connective tissue window in the side of a recipient nerve in reverse end-to-side transfers (RETS) remains controversial. OBJECTIVE: To test whether the depth of connective tissue disruption influences the efficiency of donor axonal regeneration in the context of RETS. METHODS: Sprague-Dawley rats (n = 24) were assigned to 1 of the 3 groups for obturator nerve to motor femoral nerve RETS: group 1, without epineurium opening; group 2, with epineurium only opening; and group 3, with epineurium and perineurium opening. Triple retrograde labeling was used to assess the number of motor neurons that had regenerated into the recipient motor femoral branch. Thy1-GFP rats (n = 8) were also used to visualize the regeneration pathways in the nerve transfer networks at 2- and 8-week time point using light sheet fluorescence microscopy. RESULTS: The number of retrogradely labeled motor neurons that had regenerated distally toward the target muscle was significantly higher in group 3 than that in groups 1 and 2. Immunohistochemistry validated the degree of connective tissue disruption among the 3 groups, and optical tissue clearing methods demonstrated donor axons traveling outside the fascicles in groups 1 and 2 but mostly within the fascicles in group 3. CONCLUSION: Creating a perineurial window in the side of recipient nerves provides the best chances of robust donor axonal regeneration across the RETS repair site. This finding aids nerve surgeons by confirming that a deep window should be undertaken when doing a RETS procedure.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".