Impacts of Rehabilitation Gait Training on Functional Outcomes after Tibial Nerve Transfer for Patients with Peroneal Nerve Injury: A Nonrandomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Although there was initial success using tibial nerve transfer to restore ankle dorsiflexion following peroneal nerve injury, results from later series were less promising. A potential reason is coactivation of the much stronger antagonistic muscles during gait. The purpose of this study was to test the hypothesis that gait training would improve functional performance following tibial nerve transfer. METHODS: Using a prospective, nonrandomized, controlled study design, patients were divided into two groups: surgery only or surgery plus gait training. Of the 20 patients who showed reinnervation in the tibialis anterior muscle, 10 were assigned to the gait training group, and an equal number were in the control group. Those in the treatment group began training once reinnervation in the tibialis anterior muscle was detected, whereas those in the control group continued to use their ankle-foot orthosis full time. Differences in ankle dorsiflexion were measured using the Medical Research Council scale, and quantitative force measurement and functional disability was measured using the Stanmore Scale. RESULTS: Patients in the gait training group attained significantly better functional recovery as measured by the Stanmore Scale (79.5 ± 14.3) (mean ± SD) versus (37.2 ± 3.5) in the control group (p = 0.02). Medical Research Council grades were 3.8 ± 0.6 in the training group versus 2.5 ± 1.2 in the surgery only group (p < 0.05). Average dorsiflexion force from patients with above antigravity strength (all from the training group) was 31 percent of the contralateral side. CONCLUSION: In patients with successful reinnervation following tibial nerve transfers, rehabilitation training significantly improved dorsiflexion strength and function. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, II.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".