Nerve Grafting for Axillary Nerve Injuries Following Shoulder Trauma: A Systematic Review of Surgical Outcomes
Bibliographic record
Abstract
The axillary nerve is particularly susceptible to injury following shoulder trauma such as dislocation and fracture-dislocation, leading to loss of abduction, external rotation, and sensory deficits. Severe cases with loss of continuity require surgical intervention. Nerve grafting is the mainstay when direct repair is not feasible, though outcomes are influenced by timing, graft length, and patient selection. This review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. A systematic search of PubMed, Embase, Scopus, and the Cochrane Library was performed up to August 2025 using keywords including "axillary nerve injury", "nerve grafting", "shoulder trauma", and "nerve repair". Inclusion criteria were human studies of traumatic axillary nerve injuries treated with nerve grafting, with at least 12 months' follow-up and functional outcomes reported. Case reports, animal studies, and series with fewer than five patients were excluded. Two independent reviewers performed study selection, data extraction, and risk of bias assessment using the Newcastle-Ottawa Scale (NOS) and JBI checklist. A total of six studies comprising 223 patients were included. Early nerve grafting (≤4-6 months from injury) yielded the best results, with 70-85% achieving at least Medical Research Council (MRC) grade ≥3 deltoid strength, and many regaining M4-M5 with preserved long-term function. Shorter grafts (<6 cm) and younger age predicted superior recovery, while delayed repairs correlated with persistent weakness and atrophy. Comparative series showed nerve grafting and nerve transfers achieved broadly equivalent functional outcomes, though grafting demonstrated slightly higher objective strength. Nerve grafting provides reliable restoration of deltoid strength and shoulder abduction after traumatic axillary nerve injury, particularly when performed within three to six months and with shorter graft lengths. While nerve transfers remain a valid alternative in select cases, grafting preserves native neural pathways and supports durable functional recovery. Larger prospective studies with standardized metrics are required to establish evidence-based guidelines.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".