Promising neuroprotective strategies for traumatic spinal cord injury with a focus on the differential effects among anatomical levels of injury
Bibliographic record
Abstract
Traumatic spinal cord injury (SCI) is a devastating condition of motor, sensory, and autonomic dysfunction. The significant cost associated with the management and lifetime care of patients with SCI also presents a major economic burden. For these reasons, there is a need to develop and translate strategies that can improve outcomes following SCI. Given the challenges in achieving regeneration of the injured spinal cord, neuroprotection has been at the forefront of clinical translation. Yet, despite many preclinical advances, there has been limited translation into the clinic apart from methylprednisolone (which remains controversial), hypertensive therapy to maintain spinal cord perfusion, and early decompressive surgery. While there are several factors related to the limited translational success, including the clinical and mechanistic heterogeneity of human SCI, the misalignment between animal models of SCI and clinical reality continues to be an important factor. Whereas most clinical cases are at the cervical level, only a small fraction of preclinical research is conducted in cervical models of SCI. Therefore, this review highlights the most promising neuroprotective and neural reparative therapeutic strategies undergoing clinical assessment, including riluzole, hypothermia, granulocyte colony-stimulating factor, glibenclamide, minocycline, Cethrin (VX-210), and anti-Nogo-A antibody, and emphasizes their efficacy in relation to the anatomical level of injury. Our hope is that more basic research will be conducted in clinically relevant cervical SCI models in order to expedite the transition of important laboratory discoveries into meaningful treatment options for patients with 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".