Nerve transfer to musculocutaneous for elbow flexion restoration in brachial plexus injury (Ulnar and/or Median vs. Intercostal): A systematic review and meta-analysis of comparative studies
Bibliographic record
Abstract
BACKGROUND: Nerve transfers are a cornerstone in the surgical management of traumatic brachial plexus injuries (BPIs) to restore elbow flexion. Common donor nerves include intraplexal sources like the ulnar and median nerves (fascicular transfers) and extraplexal sources like the intercostal nerves (ICNs). Despite the widespread use of both techniques, the optimal donor nerve remains a subject of debate. This systematic review and meta-analysis aims to compare these techniques for restoring elbow flexion in BPIs. METHODS: A systematic search was conducted across PubMed, Embase, Cochrane Library, Scopus, and Web of Science to identify comparative studies. The quality of the studies included was assessed using the Newcastle-Ottawa Scale (NOS). Meta-analyses were performed to compare motor recovery (≥ M3), time to M3 recovery, and complication rates between the two surgical approaches. RESULTS: The analysis included 13 studies with a total of 537 patients. In the overall cohort, which included mixed injury patterns, fascicular transfers showed a statistically significant advantage for achieving ≥ M3 recovery (RR = 0.84, 95% CI [0.75, 0.94]). However, when the analysis was restricted to patients with only upper-BPIs, there was no significant difference in achieving ≥ M3 strength between fascicular and ICN transfers (RR = 0.92, 95% CI [0.82, 1.04]). Fascicular transfers resulted in a significantly faster time to ≥ M3 recovery by approximately five months (MD = 5.25, 95% CI [2.87, 7.62]). Donor-site morbidity (18 sensory, 10 motor deficits) and wrist co-flexion were reported in fascicular transfer groups, whereas pneumothorax (4 cases) was the primary complication for ICN transfers. CONCLUSION: In patients with upper-BPIs, fascicular and ICN transfers yield comparable elbow flexion strength. The choice of procedure is a trade-off between the faster recovery offered by fascicular transfers and the better rehabilitation course of ICN transfers.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".