Multipoint Stimulation Motor Unit Number Estimation of the Extensor Indicis and Anconeus After Spinal Cord Injury
Bibliographic record
Abstract
INTRODUCTION/AIMS: Cervical spinal cord injury (SCI) significantly impairs upper limb function, affecting patients' quality of life. Nerve transfer surgery can restore arm and hand function, but its success depends on the health of infralesional lower motor neurons (LMNs). LMN abnormalities are prevalent in muscles targeted for nerve transfer, particularly those innervated by the posterior interosseous nerve (PIN) and radial nerve, essential for wrist extension and hand opening. This study evaluates the health of infralesional LMNs in cervical SCI using multipoint stimulation motor unit number estimation (MPS-MUNE). We assessed motor unit counts in the C7-innervated anconeus and the predominantly C8-innervated extensor indicis (EI) to determine their viability as targets for nerve transfer surgery. METHODS: We conducted a prospective, two-center cohort study using MPS-MUNE to evaluate 15 individuals with cervical SCI (26 limbs) and 17 healthy controls. RESULTS: Compound muscle action potential (CMAP) and MUNE values were significantly lower in SCI patients compared to controls (EI CMAP: 2.0 mV ± 1.57, EI MUNE: 33 ± 30.5; anconeus CMAP: 2.7 mV ± 1.9, anconeus MUNE: 39 ± 50.6 versus controls: EI CMAP: 6.6 mV ± 1.0, EI MUNE: 137 ± 33.9; anconeus CMAP: 6.6 mV ± 1.3, anconeus MUNE: 146 ± 42.3). Test-retest reliability for EI and anconeus were 0.84 (CI: 0.45-0.95) and 0.78 (CI: 0.36-0.93), respectively. DISCUSSION: Significant LMN loss was observed after cervical SCI. MPS-MUNE shows potential for evaluating LMN health, highlighting its importance for assessing nerve transfer targets and planning interventions.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".