Thoracic Spinal Cord Hemisection Surgery and Open-Field Locomotor Assessment in the Rat
Bibliographic record
Abstract
Spinal cord injury (SCI) causes disturbances in motor, sensory, and autonomic function below the level of the lesion. Experimental animal models are valuable tools to understand the neural mechanisms involved in locomotor recovery after SCI and to design therapies for clinical populations. There are several experimental SCI models including contusion, compression, and transection injuries that are used in a wide variety of species. A hemisection involves the unilateral transection of the spinal cord and disrupts all ascending and descending tracts on one side only. Spinal hemisection produces a highly selective and reproducible injury in comparison to contusion or compression techniques that is useful for investigating neural plasticity in spared and damaged pathways associated with functional recovery. We present a detailed step-by-step protocol for performing a thoracic hemisection at the T8 vertebral level in the rat that results in an initial paralysis of the hindlimb on the side of the lesion with graded spontaneous recovery of locomotor function over several weeks. We also provide a locomotor scoring protocol to assess functional recovery in the open-field. The locomotor assessment provides a linear recovery profile and can be performed both early and repeatedly after injury in order to accurately screen animals for appropriate time points in which to conduct more specialized behavioral testing. The hemisection technique presented can be readily adapted to other transection models and species, and the locomotor assessment can be used in a variety of SCI and other injury models to score locomotor function.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".