Upper Extremity Nerve Transfers for Treatment of Nerve Injury After Cervical Spine Surgery
Bibliographic record
Abstract
PURPOSE: Nerve transfers to restore or augment function after spinal cord injury is an expanding field. There is a paucity of information, however, on the use of nerve transfers for patients having undergone spine surgery. The incidence of neurologic deficit after spine surgery is rare but extremely debilitating. The purpose of this study was to describe the functional benefit after upper extremity nerve transfers in the setting of nerve injury after cervical spine surgery. METHODS: A single-center retrospective review of all patients who underwent nerve transfers after cervical spine surgery was completed. Patient demographics, injury features, spine surgery procedure, nerve conduction and electromyography study results, time to referral to nerve surgeon, time to surgery, surgical technique and number of nerve transfers performed, complications, postoperative muscle testing, and subjective outcomes were reviewed. RESULTS: Fourteen nerve transfers were performed in 6 patients after cervical spine surgery. Nerve transfer procedures consisted of a transfer between a median nerve branch of flexor digitorum superficialis into a biceps nerve branch, an ulnar nerve branch of flexor carpi ulnaris into a brachialis nerve branch, a radial nerve branch of triceps muscle into the axillary nerve, and the anterior interosseous nerve into the ulnar motor nerve. Average patient age was 55 years; all patients were male and underwent surgery on their left upper extremity. Average referral time was 7 months, average time to nerve transfer was 9 months, and average follow-up was 21 months. Average preoperative muscle grading was 0.9 of 5, and average postoperative muscle grading was 4.1 of 5 ( P < 0.00001). CONCLUSIONS: Upper extremity peripheral nerve transfers can significantly help patients regain muscle function from deficits secondary to cervical spine procedures. The morbidity of the nerve transfers is minimal with measurable improvements in muscle function.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".