Pharmacological Treatment of Degenerative Cervical Myelopathy: A Critical Review of Current Evidence
Bibliographic record
Abstract
Degenerative cervical myelopathy (DCM) is the leading cause of spinal cord dysfunction in adults, representing substantial morbidity and significant financial and resource burdens. Typically, patients with progressive DCM will eventually receive surgical treatment. Nonetheless, despite advancements in pharmacotherapeutics, evidence for pharmacological therapy remains limited. Health professionals from various fields would find interest in pharmacological agents that could benefit patients with mild DCM or enhance surgical outcomes. This review aims to consolidate all clinical and experimental evidence on the pharmacological treatment of DCM. We conducted a comprehensive narrative review that presents all pharmacological agents that have been investigated for DCM treatment in both humans and animal models. Riluzole exhibits effectiveness solely in rat models, but not in treating mild DCM in humans. Cerebrolysin emerges as a potential neuroprotective agent for myelopathy in animals but had contradictory results in clinical trials. Limaprost alfadex demonstrates motor function improvement in animal models and exhibits promising outcomes in a small clinical trial. Glucocorticoids not only fail to provide clinical benefits but may also lead to adverse events. Cilostazol, anti-Fas ligand antibody, and Jingshu Keli display promise in animal studies, while erythropoietin, granulocyte colony-stimulating factor and limaprost alfadex exhibit potential in both animal and human research. Existing evidence mainly rests on weak clinical data and animal experimentation. Current pharmacological efforts target ion channels, stem cell differentiation, inflammatory, vascular, and apoptotic pathways. The inherent nature and pathogenesis of DCM offer substantial prospects for developing neurodegenerative or neuroprotective therapies capable of altering disease progression, potentially delaying surgical intervention, and optimizing outcomes for those undergoing surgical decompression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".