Axon counts of potential nerve transfer donors for peroneal nerve reconstruction
Bibliographic record
Abstract
BACKGROUND:The common peroneal nerve is the most commonly injured nerve in the lower limb.Nerve transfer using expendable donor nerves is emerging in the literature as an alternative surgical procedure to traditional treatments.OBJECTIVE: To identify potential donors of motor axons from the tibial nerve that can be transferred to the common peroneal nerve branches.METHODS: Using 10 human cadaveric lower extremities, all motor nerve branches of the tibial nerve were identified and biopsied.These were compared with the motor branches to tibialis anterior and extensor hallucis longus (branches of the deep peroneal nerve). RESULTS:The most suitable donor nerves with respect to cross-sectional area to tibialis anterior (cross sectional area [mean ± SD] 0.255±0.111mm) was the motor branch to lateral gastrocnemius (0.256±0.105 mm).When comparing the total number of axons, the branch to the tibialis anterior had a mean of 3363±1997 axons.The branch to the popliteus was most similar, with 3317±1467 axons.The most suitable donor nerves for the motor branch to extensor hallucis longus (cross sectional area 0.197±0.302mm) with respect to cross-sectional area was the motor branch to flexor hallucis longus (0.234±0.147 mm).When comparing the total number of axons, the branch to the extensor hallucis longus had an average of 2062±2314 axons.The branch to the lateral gastrocnemius was most similar with 2352±1249 axons and was a suitable donor.CONCLUSION: Nerve transfers should be included in the armamentarium for lower extremity reinnervation, as it is in the upper limb.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".