The Use of Nerve Transfers to Restore Upper Extremity Function in Cervical Spinal Cord Injury
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Bibliographic record
Abstract
BACKGROUND: Nerve transfer surgery to restore upper extremity function in cervical spinal cord injury (SCI) is novel and may transform treatment. Determining candidacy even years post-SCI is ill defined and deserves investigation. OBJECTIVE: To develop a diagnostic algorithm, focusing on electrodiagnostic (EDX) studies, to determine eligibility for nerve transfer surgery. DESIGN: Retrospective descriptive case series. SETTING: Tertiary university-based institution. PATIENTS: Individuals with cervical SCI (n = 45). METHODS: The electronic medical records of people referred to the Plastic Surgery Multidisciplinary Upper Extremity Surgery in SCI clinic from 2010-2015 were reviewed. People were considered for nerve transfers to restore elbow extension or finger flexion and/or extension. Data including demographic, clinical evaluation, EDX results, surgery, and outcomes were collected and analyzed. MAIN OUTCOME MEASUREMENTS: EDX data, including nerve conduction studies and electromyography, for bilateral upper extremities of each patient examined was used to assess for the presence of lower motor neuron injury, which would preclude late nerve transfer. RESULTS: Based on our criteria and the results of EDX testing, a substantial number of patients presenting even years post-SCI were candidates for nerve transfers. Clinical outcome results are heterogeneous but promising and suggest that further refinement of eligibility, long-term follow-up, and standardized assessment will improve our understanding of the role of nerve transfer surgery to restore function in people with midcervical SCI. CONCLUSIONS: Many patients living with SCI are candidates for nerve transfer surgery to restore upper extremity function. Although the ultimate efficacy of these surgeries is not yet determined, this study attempts to report the criteria we are using and may ultimately determine the timing for intervention and which transfers are most useful for this heterogeneous population. LEVEL OF EVIDENCE: IV.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 it