Testing the effectiveness and the contribution of experimental supercharge (reversed) end-to-side nerve transfer
Bibliographic record
Abstract
OBJECTIVE: Supercharge end-to-side (SETS) transfer, also referred to as reverse end-to-side transfer, distal to severe nerve compression neuropathy or in-continuity nerve injury is gaining clinical popularity despite questions about its effectiveness. Here, the authors examined SETS distal to experimental neuroma in-continuity (NIC) injuries for efficacy in enhancing neuronal regeneration and functional outcome, and, for the first time, they definitively evaluated the degree of contribution of the native and donor motor neuron pools. METHODS: This study was conducted in 2 phases. In phase I, rats (n = 35) were assigned to one of 5 groups for unilateral sciatic nerve surgeries: group 1, tibial NIC with distal peroneal-tibial SETS; group 2, tibial NIC without SETS; group 3, intact tibial and severed peroneal nerves; group 4, tibial transection with SETS; and group 5, severed tibial and peroneal nerves. Recovery was evaluated biweekly using electrophysiology and locomotion tasks. At the phase I end point, after retrograde labeling, the spinal cords were analyzed to assess the degree of neuronal regeneration. In phase II, 20 new animals underwent primary retrograde labeling of the tibial nerve, following which they were assigned to one of the following 3 groups: group 1, group 2, and group 4. Then, secondary retrograde labeling from the tibial nerve was performed at the study end point to quantify the native versus donor regenerated neuronal pool. RESULTS: In phase I studies, a significantly increased neuronal regeneration in group 1 (SETS) compared with all other groups was observed, but with modest (nonsignificant) improvement in electrophysiological and behavioral outcomes. In phase II experiments, the authors discovered that secondary labeling in group 1 was predominantly contributed from the donor (peroneal) pool. Double-labeling counts were dramatically higher in group 2 than in group 1, suggestive of hampered regeneration from the native tibial motor neuron pool across the NIC segment in the presence of SETS. CONCLUSIONS: SETS is indeed an effective strategy to enhance axonal regeneration, which is mainly contributed by the donor neuronal pool. Moreover, the presence of a distal SETS coaptation appears to negatively influence neuronal regeneration across the NIC segment. The clinical significance is that SETS should only employ synergistic donors, as the use of antagonistic donors can downgrade recovery.
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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 | 0.002 | 0.002 |
| 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".