Strategies to Promote Neural Repair and Regeneration After Spinal Cord Injury
Bibliographic record
Abstract
STUDY DESIGN: Retrospective review of current literature regarding neuroprotection and axonal regeneration therapies for acute spinal cord injury. OBJECTIVES: To provide an update for spine clinicians of the emerging therapeutic strategies for promoting neural repair and regeneration after spinal cord injury. SUMMARY OF BACKGROUND DATA: The neuroscientific community has generated a number of novel potential treatments for spinal injuries, some of which have entered clinical trials. Clinicians who manage spinal cord trauma are likely to encounter patients and their families who have questions or wish to be involved in these emerging treatments. METHODS: Literature review, with particular focus on currently used medications that may have neuroprotective potential in spinal cord injury, and axonal regeneration strategies that are emerging in preliminary human clinical trials. RESULTS: A number of medications such as erythropoietin and minocycline have demonstrated neuroprotective properties in animal models of spinal cord injury, and their long-established safety in humans make them appealing candidates for clinical trials. Human experience with novel neuroprotective and axonal regeneration strategies is growing around the world, and the peer-reviewed reporting of this is anxiously awaited. CONCLUSIONS: The initiation of human clinical trials for spinal cord-injured patients heralds great hope that effective therapies will be forthcoming, although a great deal remains to be learned. Clinicians must provide leadership in the epidemiologic design and rigor of these initial forays into human evaluation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".