Preoperative electrodiagnostic planning for upper limb peripheral nerve transfers in cervical spinal cord injury: A narrative review
Bibliographic record
Abstract
Peripheral nerve transfer (PNT) to improve upper limb function following cervical spinal cord injury (SCI) involves the transfer of supralesional donor nerves under voluntary control to intralesional or sublesional lower motor neurons not under voluntary control. Appropriate selection of donor and recipient nerves and surgical timing affect functional outcomes. Although the gold standard of nerve selection is intraoperative nerve stimulation, preoperative electrodiagnostic (EDX) evaluation may help guide surgical planning. Currently there is no standardized preoperative EDX protocol. This study reviews the EDX workup preceding PNT surgery in cervical SCI and proposes an informed EDX protocol to assist with surgical planning. The PICO (Population, Intervention, Comparison, Outcome) framework was used to formulate relevant Medical Subject Headings (MeSH) terms and identify published cases of PNT in cervical SCI in Medline, Embase, CINAHL, and Emcare databases in the last 10 years. The EDX techniques evaluating putative donor nerves, recipient nerve branches, time sensitivity of nerve transfer and other electrophysiological parameters were summarized to guide creation of a preoperative EDX protocol. Needle electromyography (EMG) was the most commonly used EDX technique to identify healthy donor nerves. Although needle EMG has also been used on recipient nerves, compound muscle action potential amplitudes may provide a more accurate determination of recipient nerve health and time sensitivity for nerve transfer. Although there has been progress in presurgical EDX evaluation, EMG and nerve conduction study approaches are variable, and each has limitations in their utility for preoperative planning. There is need for standardization in the EDX evaluation preceding PNT surgery to assist with donor and recipient nerve selection, surgical timing and to optimize outcomes. Based on results of this review, herein we propose the PreSCIse (PRotocol for Electrodiagnosis in SCI Surgery of the upper Extremity) preoperative EDX panel to achieve said goals through an interdisciplinary and patient-centered approach.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".