Suprascapular Nerve Reconstruction in Obstetrical Brachial Plexus Palsy: Spinal Accessory Nerve Transfer versus C5 Root Grafting
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to determine whether there is any difference in external rotation following reconstruction of the suprascapular nerve using nerve grafts from the proximal C5 root or nerve transfer using the spinal accessory nerve. METHODS: External rotation was assessed using the Active Movement Scale immediately before surgery and 3 years postoperatively. Patients with less than 3 years of follow-up were excluded. For patients who underwent secondary shoulder surgery before the 3-year follow-up, the Active Movement Scale score before shoulder surgery was used as the outcome. RESULTS: One-hundred-six patients underwent nerve grafting, while 71 patients underwent spinal accessory nerve transfer. The spinal accessory nerve transfer group had a greater proportion of patients with total plexus palsies, more avulsions, and an earlier age at surgery (p < 0.001). In the C5 nerve graft group, the mean Active Movement Scale score increased from 0.4 to 2.2 (p < 0.001). In the nerve transfer group, the mean score increased from 0.2 to 3.0 (p < 0.001). Preoperatively, the C5 nerve graft group had significantly better scores than the nerve transfer group (p = 0.03). Postoperatively, there was no significant difference between treatments (p = 0.1). Further statistical analysis failed to demonstrate a significant advantage of one surgical treatment over the other. CONCLUSIONS: There was no difference in external rotation after suprascapular nerve reconstruction with either nerve grafting from the proximal C5 root or spinal accessory nerve transfer. The choice of suprascapular nerve reconstruction can be selected depending on specific requirements of the individual lesion.
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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 | 0.001 | 0.002 |
| 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) | 0.002 | 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".