Traumatic Spinal Cord Injury—Repair and Regeneration
Bibliographic record
Abstract
BACKGROUND: Traumatic spinal cord injuries (SCI) have devastating consequences for the physical, financial, and psychosocial well-being of patients and their caregivers. Expediently delivering interventions during the early postinjury period can have a tremendous impact on long-term functional recovery. PATHOPHYSIOLOGY: This is largely due to the unique pathophysiology of SCI where the initial traumatic insult (primary injury) is followed by a progressive secondary injury cascade characterized by ischemia, proapoptotic signaling, and peripheral inflammatory cell infiltration. Over the subsequent hours, release of proinflammatory cytokines and cytotoxic debris (DNA, ATP, reactive oxygen species) cyclically adds to the harsh postinjury microenvironment. As the lesions mature into the chronic phase, regeneration is severely impeded by the development of an astroglial-fibrous scar surrounding coalesced cystic cavities. Addressing these challenges forms the basis of current and upcoming treatments for SCI. MANAGEMENT: This paper discusses the evidence-based management of a patient with SCI while emphasizing the importance of early definitive care. Key neuroprotective therapies are summarized including surgical decompression, methylprednisolone, and blood pressure augmentation. We then review exciting neuroprotective interventions on the cusp of translation such as Riluzole, Minocycline, magnesium, therapeutic hypothermia, and CSF drainage. We also explore the most promising neuroregenerative strategies in trial today including Cethrin™, anti-NOGO antibody, cell-based approaches, and bioengineered biomaterials. Each section provides a working knowledge of the key preclinical and patient trials relevant to clinicians while highlighting the pathophysiologic rationale for the therapies. CONCLUSION: We conclude with our perspectives on the future of treatment and research in this rapidly evolving field.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".