Comparing the Efficacy of Triple Nerve Transfers with Nerve Graft Reconstruction in Upper Trunk Obstetric Brachial Plexus Injury
Bibliographic record
Abstract
BACKGROUND: Upper trunk obstetric brachial plexus injury can cause profound shoulder and elbow dysfunction. Although neuroma excision with interpositional sural nerve grafting is the current gold standard, distal nerve transfers have a number of potential advantages. The goal of this study was to compare the clinical outcomes and health care costs between nerve grafting and distal nerve transfers in children with upper trunk obstetric brachial plexus injury. METHODS: In this prospective cohort study, children who underwent triple nerve transfers were followed with the Active Movement Scale for 2 years. Their outcomes were compared to those of children who underwent nerve graft reconstruction. To assess health care use, a cost analysis was also performed. RESULTS: Twelve patients who underwent nerve grafting were compared to 14 patients who underwent triple nerve transfers. Both groups had similar baseline characteristics and showed improved shoulder and elbow function following surgery. However, the nerve transfer group displayed significantly greater improvement in shoulder external rotation and forearm supination 2 years after surgery (p < 0.05). The operative time and length of hospital stay were significantly lower (p < 0.05), and the overall cost was approximately 50 percent less in the nerve transfer group. CONCLUSION: Triple nerve transfer for upper trunk obstetric brachial plexus injury is a feasible option, with better functional shoulder external rotation and forearm supination, faster recovery, and lower cost compared with traditional nerve graft reconstruction. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, II.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".