Use of Peripheral Nerve Transfers in Tetraplegia: Evaluation of Feasibility and Morbidity
Bibliographic record
Abstract
BACKGROUND: Peripheral nerve transfers are being used to improve upper extremity function in cervical spinal cord injury (SCI) patients. The purpose of this study was to evaluate feasibility and perioperative complications following these procedures. METHODS: Eligible SCI patients with upper extremity dysfunction were assessed and followed for a minimum of 3 months after surgery. Data regarding demographics, medical history, physical examination, electrodiagnostic testing, intraoperative nerve stimulation, recipient nerve histomorphometry, surgical procedure, and complications were collected. RESULTS: Seven patients had surgery on eight limbs, mean age of 28 ± 9.9 years and mean time from SCI injury of 5.1 ± 5.2 years. All patients had volitional elbow flexion and no volitional hand function. The nerve to the brachialis muscle was used as the expendable donor, and the recipients included the anterior interosseous nerve (AIN) (for volitional prehension), nerve branches to the flexor carpi radialis, and flexor digitorum superficialis. Two patients underwent additional nerve transfers: (1) supinator to extensor carpi ulnaris or (2) deltoid to triceps. No patients had any loss of baseline upper extremity function, seven of eight AIN nerve specimens had preserved micro-architecture, and all intraoperative stimulation of recipient neuromuscular units was successful further supporting feasibility. Four patients had perioperative complications; all resolved or improved (paresthesias). CONCLUSION: Nerve transfers can be used to reestablish volitional control of hand function in SCI. This surgery does not downgrade existing function, uses expendable donor nerve, and has no postoperative immobilization, which might make it a more viable option than traditional tendon transfer and other procedures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".