Spinal cord injury: overview of experimental approaches used to restore locomotor activity
Bibliographic record
Abstract
Spinal cord injury affects more than 2.5 million people worldwide and can lead to paraplegia and quadriplegia. Anatomical discontinuity in the spinal cord results in disruption of the impulse conduction that causes temporary or permanent changes in the cord's normal functions. Although axonal regeneration is limited, damage to the spinal cord is often accompanied by spontaneous plasticity and axon regeneration that help improve sensory and motor skills. The recovery process depends mainly on synaptic plasticity in the preexisting circuits and on the formation of new pathways through collateral sprouting into neighboring denervated territories. However, spontaneous recovery after spinal cord injury can go on for several years, and the degree of recovery is very limited. Therefore, the development of new approaches that could accelerate the gain of motor function is of high priority to patients with damaged spinal cord. Although there are no fully restorative treatments for spinal injury, various rehabilitative approaches have been tested in animal models and have reached clinical trials. In this paper, a closer look will be given at the potential therapies that could facilitate axonal regeneration and improve locomotor recovery after injury to the spinal cord. This article highlights the application of several interventions including locomotor training, molecular and cellular treatments, and spinal cord stimulation in the field of rehabilitation research. Studies investigating therapeutic approaches in both animal models and individuals with injured spinal cords will be presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".