Distal Nerve Transfers Are Effective in Treating Patients with Upper Trunk Obstetrical Brachial Plexus Injuries
Bibliographic record
Abstract
BACKGROUND: Current surgical management of obstetrical brachial plexus injury is primary reconstruction with sural nerve grafts. Recently, the nerve-to-nerve transfer technique has been used to treat brachial plexus injury in adults, affording the benefit of distal coaptations that minimize regenerative distance. The purpose of this study was to test the hypothesis that nerve transfers are effective in reconstructing isolated upper trunk obstetrical brachial plexus injuries. METHODS: Ten patients aged 10 to 18 months were treated with three nerve transfers: spinal accessory nerve to the suprascapular nerve for shoulder abduction and external rotation; a radial to axillary nerve for shoulder abduction; and ulnar or median nerve transfer to the musculocutaneous nerve for elbow flexion. Patients were assessed preoperatively and postoperatively using the Active Movement Scale. All patients were followed regularly for up to 2 years. RESULTS: Improvement in elbow and shoulder function was observed between 6 and 24 months. By 6 months, all patients passed the cookie test. At 24 months, shoulder abduction improved from 3.7 ± 0.6 to 5.0 ± 0.5, shoulder external rotation from 1.8 ± 0.4 to 4.3 ± 0.6, shoulder flexion from 3.7 ± 0.5 to 5.4 ± 0.5, elbow flexion from 3.7 ± 0.6 to 6.3 ± 0.2, and forearm supination from 2.1 ± 0.4 to 5.9 ± 0.2. There was no clinically appreciable donor-site morbidity. CONCLUSIONS: Nerve transfers reduced operative times compared with traditional nerve grafting procedures. Those patients showed significant gains in Active Movement Scale score by 24 months postoperatively, comparable to results achieved by nerve grafting. These findings support nerve transfers as a potential alternative treatment option for upper trunk obstetrical brachial plexus injuries. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".