Optimizing nerve transfer surgery in tetraplegia: clinical decision making based on innervation patterns in spinal cord injury
Bibliographic record
Abstract
OBJECTIVE: Nerve transfers are increasingly being utilized in the treatment of chronic tetraplegia, with increasing literature describing significant improvements in sensorimotor function up to years after injury. However, despite technical advances, clinical outcomes remain heterogenous. Preoperative electrodiagnostic testing is the most direct measure of nerve health and may provide prognostic information that can optimize preoperative patient selection. The objective of this study in patients with spinal cord injury (SCI) was to determine various zones of injury (ZOIs) via electrodiagnostic assessment (EDX) to predict motor outcomes after nerve transfers in tetraplegia. METHODS: This retrospective review of prospectively collected data included all patients with tetraplegia from cervical SCI who underwent nerve transfer at the authors' institution between 2013 and 2020. Preoperative demographic data, results of EDX, operative details, and postoperative motor outcomes were extracted. EDX was standardized into grades that describe donor and recipient nerves. Five zones of SCI were defined. Motor outcomes were then compared based on various zones of innervation. RESULTS: Nineteen tetraplegic patients were identified who underwent 52 nerve transfers targeting hand function, and 75% of these nerve transfers were performed more than 1 year postinjury, with a median interval to surgery following SCI of 24 (range 8-142) months. Normal recipient compound muscle action potential and isolated upper motor neuron injury on electromyography (EMG) were associated with greater motor recovery. When nerve transfers were stratified based on donor EMG, greater motor gains were associated with normal than with abnormal donor EMG motor unit recruitment patterns. When nerve transfers were separated based on donor and recipient nerves, normal flexor donors were more crucial than normal extensor donors in powering their respective flexor recipients. CONCLUSIONS: This study elucidates the relationship of the preoperative innervation zones in SCI patients to final motor outcomes. EDX studies can be used to tailor surgical therapies for nerve transfers in patients with tetraplegia. The authors propose an algorithm for optimizing nerve transfer strategies in tetraplegia, whereby understanding the ZOI and grade of the donor/recipient nerve is critical to predicting motor outcomes.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".