Nerve Transfers to Restore Upper Extremity Function in Cervical Spinal Cord Injury
Bibliographic record
Abstract
BACKGROUND: Cervical spinal cord injury can result in profound loss of upper extremity function. Recent interest in the use of nerve transfers to restore volitional control is an exciting development in the care of these complex patients. In this article, the authors review preliminary results of nerve transfers in spinal cord injury. METHODS: Review of the literature and the authors' cases series of 13 operations in nine spinal cord injury nerve transfer recipients was performed. Representative cases were reviewed to explore critical concepts and preliminary outcomes. RESULTS: The nerve transfers used expendable donors (e.g., teres minor, deltoid, supinator, and brachialis) innervated above the level of the spinal cord injury to restore volitional control of missing function such as elbow extension, wrist extension, and/or hand function (posterior interosseous nerve or anterior interosseous nerve/finger flexors reinnervated). Results from the literature and the authors' patients (after a mean postsurgical follow-up of 12 months) indicate gains in function as assessed by both manual muscle testing and patients' self-reported outcomes measures. CONCLUSIONS: Nerve transfers can provide an alternative and consistent means of reestablishing volitional control of upper extremity function in people with cervical level spinal cord injury. Early outcomes provide evidence of substantial improvements in self-reported function despite relatively subtle objective gains in isolated muscle strength. Further work to investigate the optimal timing and combination of nerve transfer operations, the combination of these with traditional treatments (tendon transfer and functional electrical stimulation), and measurement of outcomes is imperative for determining the precise role of these operations. 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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".