Animal Models Used in Spinal Cord Regeneration Research
Bibliographic record
Abstract
STUDY DESIGN: A literature review was conducted. OBJECTIVES: To review animal models and injury paradigms used in the neurobiologic study of spinal cord regeneration, and to assist the spinal clinician in interpreting the many encouraging reports of potential therapies emerging from basic science laboratories. SUMMARY OF BACKGROUND DATA: An enormous amount of interest in spinal cord regeneration research has been generated within the past 20 years with the hope that experimental therapies will become available for individuals with spinal cord injuries. The use of various animal models in the laboratory setting has been critical to the development of such experimental therapies. METHODS: A literature review was conducted. RESULTS: Experimental interventions in animal models of spinal cord injury were evaluated both anatomically and functionally. Anatomic assessments use various histologic techniques and frequently include the use of anterograde and retrograde axonal tracers. Functional assessments can be performed neurophysiologically or by the observation of motor and sensory performance on a number of different tests. Sharp spinal cord injury paradigms in which the cord is completely or partially transected are useful for assessing axonal regeneration anatomically. In contrast, blunt injury models in which the cord is compressed or contused more accurately mimic the typical human injury and provide a good setting for the study of secondary pathophysiologic processes immediately after injury. CONCLUSIONS: Animal models will continue to play a critical role in the development of experimental therapies for spinal cord injuries. Both sharp and blunt spinal cord injury paradigms have unique characteristics that make them useful in addressing slightly different neurobiologic problems.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".