Partial Tibial Nerve Transfer to the Tibialis Anterior Motor Branch to Treat Peroneal Nerve Injury After Knee Trauma
Bibliographic record
Abstract
BACKGROUND: Injuries to the deep peroneal nerve result in tibialis anterior muscle paralysis and associated loss of ankle dorsiflexion. Nerve grafting of peroneal nerve injuries has led to poor function; therefore, tendon transfers and ankle-foot orthotics have been the standard treatment for foot drop. QUESTIONS/PURPOSES: We (1) describe an alternative surgical technique to obtain ankle dorsiflexion by partial tibial nerve transfer to the motor branch of the tibialis anterior muscle; (2) evaluate ankle dorsiflexion strength using British Medical Research Council grading after nerve transfer; and (3) qualitatively determine factors that influence functional success of surgery. METHODS: We retrospectively reviewed 11 patients treated with partial tibial nerve transfers after peroneal nerve injury. Pre- and postoperative motor strength was measured. Patients completed questionnaires regarding pre- and postoperative gait and disability. RESULTS: One patient regained Grade 4 ankle dorsiflexion, three patients regained Grade 3, one patient regained Grade 2, and two patients regained Grade 1 ankle dorsiflexion. Four patients did not regain any muscle activity. Clinically apparent motor recovery occurred an average 7.6 months postoperatively. A majority of patients (nine) could walk and participate in activities. Seven patients did not wear ankle-foot orthotics and four patients did not limp. The donor deficits included weak toe flexion (two patients) and reduced calf circumference (seven patients). CONCLUSION: Our observations suggest nerve transfers to the deep peroneal nerve provide inconsistent ankle dorsiflexion strength, possibly related to the mechanism of peroneal nerve injury or delays in surgery. Despite variable strength, four patients achieved M3 or greater motor recovery, which enabled them to walk without assistive devices. LEVEL OF EVIDENCE: Level IV, therapeutic study. See Guidelines for Authors for a complete description of levels of evidence.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".