Multipoint Stimulation Motor Unit Number Estimation of the Extensor Indicis and Anconeus after Cervical Spinal Cord Injury
Bibliographic record
Abstract
ABSTRACT Introduction The health of infralesional lower motor neurons (LMNs) after a cervical spinal cord injury is frequently overlooked, despite its critical role in mediating effective clinical interventions for improving arm and hand function. Prior studies suggest high frequencies of infralesional lower motor abnormalities in muscles that are potential targets for nerve transfer surgery, a procedure that has the potential to restore upper limb function. Methods In this prospective, two-center cohort study, we used multipoint stimulation motor unit number estimation (MPS-MUNE) to evaluate the number of motor units in clinically relevant infralesional muscles, including the predominantly C7-innervated anconeus and the predominantly C8-innervated extensor indicis (EI) in 15 individuals with cervical spinal cord injury (26 limbs) and 17 healthy controls. Results Both CMAP and MUNE values were significantly lower (p < 0.05) for those with cervical spinal cord injury (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: 6.6mV±1.0, EI MUNE:137±33.9, Anconeus CMAP:6.6 mV±1.3, Anconeus MUNE: 146 ±42.3). The test-retest reliability as measured by intraclass correlation coefficient and confidence interval (CI) for the EI and anconeus were 0.84 (CI: 0.45-0.95) and 0.78 (CI: 0.36-0.93), respectively. Discussion This study shows significant loss of infralesional motor units after cervical spinal cord injury. We demonstrate the potential utility of MPS-MUNE for evaluating the health of LMNs. The LMN abnormalities observed underscore the significance of this approach to evaluating potential targets for nerve transfer surgery for the restoration of upper limb function.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".