Neuromuscular consequences of spinal cord injury: New mechanistic insights and clinical considerations
Bibliographic record
Abstract
The spinal cord facilitates communication between the brain and the body, containing intrinsic systems that work with lower motor neurons (LMNs) to manage movement. Spinal cord injuries (SCIs) can lead to partial paralysis and dysfunctions in muscles below the injury. While traditionally this paralysis has been attributed to disruptions in the corticospinal tract, a growing body of work demonstrates LMN damage is a factor. Motor units, comprising the LMN and the muscle fibers with which they connect, are essential for voluntary movement. Our understanding of their changes post-SCI is still emerging, but the health of motor units is vital, especially when considering innovative SCI treatments like nerve transfer surgery. This review seeks to collate current literature on how SCI impact motor units and explore neuromuscular clinical implications and treatment avenues. SCI reduced motor unit number estimates, and surviving motor units had impaired signal transmission at the neuromuscular junction, force-generating capacity, and excitability, which have the potential to recover chronically, yet the underlaying mechanisms are unclear. Furthermore, electrodiagnostic evaluations can aid in assessing the health lower and upper motor neurons, identify suitable targets for nerve transfer surgeries, and detect patients with time sensitive injuries. Lastly, many electrodiagnostic abnormalities occur in both chronic and acute SCI, yet factors contributing to these abnormalities are unknown. Future studies are required to determine how motor units adapt following SCI and the clinical implications of these adaptations.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".