A systematic review of peroneal nerve palsy and recovery following traumatic knee dislocation
Bibliographic record
Abstract
PURPOSE: A common peroneal nerve (CPN) palsy has been reported to complicate knee dislocations in 5-40 % of patients. Patients who suffer from a persistent foot drop have significantly worse functional outcomes. Reports on prognostic factors for nerve recovery or treatment-specific functional outcomes remain sparse in the literature. METHODS: Two independent reviewers completed a search of Medline, Embase, PubMed and the Cochrane Library from 1946 to present. Motor strength was determined using the Medical Research Council (MRC) grading system or an equivalent description. A functional recovery was defined as an MRC ≥3/5. RESULTS: The combined search of Medline, Embase, PubMed and the Cochrane Library identified 1528 abstracts. Thirteen articles met our inclusion/exclusion criteria. This included 214 CPN palsies. Functional recovery (MRC ≥3/5) following complete CPN palsy was 38.4 %. Full recovery (MRC = 5/5) following partial CPN palsy was 87.3 %. Younger age was predictive of neurologic recovery. Recovery following isolated neurologic interventions ranged from 0 to 30 %. CONCLUSIONS: A vastly different prognosis can be expected for patients who suffer an incomplete versus a complete CPN palsy. The majority of patients with an incomplete palsy will achieve a full motor recovery while <40 % of patients with a complete motor palsy will regain the ability to dorsiflex at the ankle. While neurologic interventions show promise for the future, the outcomes in knee dislocation patients remain poor. The most predictable means of reestablishing antigravity dorsiflexion in a persistent CPN palsy is a posterior tibial tendon transfer.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".