Reconstruction of the Acute Isolated Common Peroneal Nerve Palsy Using Primarily Nerve Repair and/or Nerve Graft: A Single-Center 28-Year Experience
Bibliographic record
Abstract
Background: Drop foot is a devastating problem in patients with common peroneal nerve palsy (CPN). The standard surgical treatments are categorized into three main categories: nerve-related procedures, tendon transfers, and functioning free muscle reconstruction. The outcomes of nerve surgeries such as neurolysis, nerve repair, transfer, or graft remain controversial. In this study, we present our experience in acute CPN palsy, employing neurolysis, nerve repair, and nerve grafting in a single institution. Methods: Between 1980 and 2016, 50 patients with CPN injury underwent either primary nerve repair, neurolysis, or nerve graft. Thirty-eight patients met our final inclusion criteria. The functional outcomes of CPN injury were reviewed, categorizing surgical outcomes into three groups based on total ankle ROM and muscle strength. Those with total ROM less than 20 or < M2 muscle strength, 20 to 30 or ≥M2, and ≥30 degrees or ≥M3 were believed to have poor, good, and excellent outcomes, respectively. Results: Nerve grafts shorter than 6 cm are associated with better functional outcome in CPN. Seventy-one percent of patients with nerve grafts shorter than 6 cm achieved good-excellent functional results. Furthermore, 100% of patients in the neurolysis and primary nerve repair groups achieved excellent functional outcomes. Conclusion: CPN surgery is often associated with underwhelming outcomes. With appropriate patient selection and meticulous surgical techniques, one could expect acceptable outcomes in such a patient population. The correct diagnosis and early intervention, along with postoperative immobilization and aggressive rehabilitation, are crucial for improving the outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".