Lower Motor Neuron Abnormality in Chronic Cervical Spinal Cord Injury: Implications for Nerve Transfer Surgery
Bibliographic record
Abstract
Nerve transfer surgery (NT) constitutes an exciting option to improve upper limb functions in chronic spinal cord injury (SCI), but requires intact sublesional lower motor neuron (LMN) health. The purpose of this study was to characterize patterns of LMN abnormality in nerve-muscle groups that are the potential recipients of NT, using a standardized electrodiagnostic examination, in individuals with chronic SCI (injury duration >2 years, injury levels C4–T1). The LMN abnormality was determined using a semihierarchical approach, combining the amplitude compound muscle action potential (CMAP) and abnormal spontaneous activity on needle electromyography (EMG). Ten participants (46 potential recipient muscles) were included (median age, 42.5 years; six males and four females; median duration from injury, 15.5 years). A high frequency of LMN abnormality was observed (87%), although there was substantial variation within and between individuals. No statistically significant discordance was observed between LMN abnormality on CMAP and EMG ( p = 0.24), however, 50% of muscles with normal CMAP demonstrated abnormal spontaneous activity. The high frequency of LMN abnormality in recipient nerve-muscle groups has implications to candidate selection for NT surgery in chronic SCI and supports the important role of the pre-operative electrodiagnostic examination. Our results further support the inclusion of both CMAP and needle EMG parameters for characterization of LMN health. Although the number of nerve-muscle groups with normal LMN health was small (13%), this underscores the neurophysiological potential of some patients with chronic injuries to benefit from NT surgery.
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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".