Rat Models of Spinal Cord Injury Provide Valuable Insight to Understand the Mechanism and Pathophysiology of Spinal Cord Injury
Bibliographic record
Abstract
Spinal Cord Injury (SCI) is a serious devastating global problem, which mostly affects young persons aged 16 to 30.SCI damages axonal pathways and interrupts synaptic transmission between brain and spinal cord.SCI in general can be classified as either complete injuries or incomplete injuries.Each type of SCI occurs in two phases, primary and secondary phase of SCI.The causes of SCI are diverse in origin and can result from contusion, compression, penetrations or maceration of the spinal cord.A variety of animal models including dogs, cats, guinea pig, primates and rodents have been developed to examine the mechanisms, pathophysiology and functional deficits following SCI, and also to test intervention strategies to develop effective therapies for the treatment of SCI.The most commonly used rat models of spinal cord injuries are transection models, compression models, contusion models and chemically-induced models.There is no single model that has dominated in the field of SCI research and each model has advantages and disadvantages.This review discusses the advantages and disadvantages of rat models of experimental SCI and knowledge gained from these rat models to understand the mechanisms, pathophysiology of 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".