Neuromodulation to guide circuit reorganization with regenerative therapies in upper extremity rehabilitation following cervical spinal cord injury
Bibliographic record
Abstract
Spinal cord injury (SCI) is a profoundly debilitating condition with no effective treatment to date. The complex response of the central nervous system (CNS) to injury and its limited regeneration capacity pose bold challenges for restoring function. Cervical SCIs are the most prevalent and regaining hand function is a top priority for individuals living with cervical SCI. A promising avenue for addressing this challenge arises from the emerging field of regenerative rehabilitation, which combines regenerative biology with physical medicine approaches. The hypothesis for optimizing gains in upper extremity function centers on the integration of targeted neurorehabilitation with novel cell- and stem cell-based therapies. However, the precise roles and synergistic effects of these components remain poorly understood, given the intricate nature of SCI and the diversity of regenerative approaches. This perspective article sheds light on the current state of regenerative rehabilitation for cervical SCI. Notably, preclinical research has yet to fully incorporate rehabilitation protocols that mimic current clinical practices, which often rely on neuromodulation strategies to activate spared circuits below the injury level. Therefore, it becomes imperative to comprehensively investigate the combined effects of neuromodulation and regenerative medicine strategies in animal models before translating these therapies to individuals with SCI. In cases of severe upper extremity paralysis, the advent of neuromodulation strategies, such as corticospinal tract (CST) and spinal cord stimulation, holds promise as the next frontier in enhancing the effectiveness of cell- and stem cell-based therapies. Future preclinical studies should explore this convergence of neuromodulation and regenerative approaches to unlock new possibilities for upper extremity treatment after 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".