Bibliographic record
Abstract
The purpose of this review is to discuss the achievements and perspectives regarding rehabilitation of sensorimotor functions after spinal cord injury. In the first part we discuss clinical approaches based on neuroplasticity, a term referring to all adaptive and maladaptive changes within the sensorimotor systems triggered by a spinal cord injury. Neuroplasticity can be facilitated through the training of movements with assistance as needed, and/or by electrical stimulation techniques. The success of such training in individuals with incomplete spinal cord injury critically depends on the presence of physiological proprioceptive input to the spinal cord leading to meaningful muscle activations during movement performances. The addition of rehabilitation technology, such as robotic devices allows for longer training times and provision of feedback information regarding changes in movement performance. Nevertheless, the improvement of function by such approaches for rehabilitation is limited. In the second part, we discuss preclinical approaches to restore function by compensating for the loss of descending input to spinal networks following complete spinal cord injury. This can be achieved with stimulation of spinal networks or approaches to restore their descending input. Electrical and pharmacological stimulation of spinal neural networks is still in an experimental stage; and despite promising repair studies in animal models, translations to humans up to now have not been convincing. It is likely that combinations of techniques targeting the promotion of axonal regeneration and meaningful plasticity are necessary to advance the restoration of function. In the future, refinement of animal studies may contribute to greater translational success.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".