Segmental infralesional lower motor neuron abnormalities in patients with sub-acute traumatic spinal cord injury
Bibliographic record
Abstract
Abstract The health of the infralesional lower motor neuron (LMN) has received little attention in individuals with traumatic cervical spinal cord injuries (SCI). Infralesional LMN health is clinically relevant in the context of nerve transfer surgery to restore critical upper limb functions, as those demonstrating LMN damage below the neurological level of injury may experience irreversible sequelae of denervation (e.g., atrophy, fibrosis) without timely intervention. In this two-centre retrospective cohort study, we examined the health of the infralesional LMN in individuals with traumatic cervical SCI, using data derived from the clinical electrodiagnostic examination performed early after SCI. We assessed 66 limbs in 42 individuals with traumatic cervical SCI (40 males, mean age = 43.6±17.2, mean duration from injury = 3.3±1.5 months, 25 with motor complete injuries). Analysis was stratified by injury level as 1) C4 and above, 2) C5 and 3) C6-7. EMG performed on representative muscles from C5-6, C6-7, C7-8 and C8-T1, were included in analysis. LMN abnormality was dichotomized as present (abnormal spontaneous activity) or absent. Data were pooled for the most caudal infralesional segment (C8-T1). Overall, a high frequency of denervation potentials was seen in all infralesional segments for all injury levels. The pooled frequency of denervation potentials at C8-T1 was 74.6% of limbs tested. There was also evidence of denervation potentials at the rostral border of the neurological level of injury, as high as 64.3% of C5-6 muscles for C5 injuries. These data support a high prevalence of infralesional LMN abnormality following SCI, which has implications to candidacy, timing of the intervention, donor nerve options and motor prognosis following SCI.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".