Characterization of a Novel, Magnetic Resonance Imaging-Compatible Rodent Model Spinal Cord Injury Device
Bibliographic record
Abstract
Rodent models of acute spinal cord injury (SCI) are often used to investigate the effects of injury mechanism, injury speed, and cord displacement magnitude, on the ensuing cascade of biological damage in the cord. However, due to its small size, experimental observations have largely been limited to the gross response of the cord. To properly understand the relationship between mechanical stimulus and biological damage, more information is needed about how the constituent tissues of the cord (i.e., gray and white matter) respond to injurious stimuli. To address this limitation, we developed a novel magnetic resonance imaging (MRI)-compatible test apparatus that can impose either a contusion-type or dislocation-type acute cervical SCI in a rodent model and facilitate MR-imaging of the cervical spinal cord in a 7 T MR scanner. In this study, we present the experimental performance parameters of the MR rig. Utilizing cadaveric specimens and static radiographs, we report contusion magnitude accuracy that for a desired 1.8 mm injury, a nominal 1.78 mm injury (SD = 0.12 mm) was achieved. High-speed video analysis was employed to determine the injury speeds for both mechanisms and were found to be 1147 mm/s (SD = 240 mm/s) and 184 mm/s (SD = 101 mm/s) for contusion and dislocation injuries, respectively. Furthermore, we present qualitative pilot data from a cadaveric trial, employing the MR rig, to show the expected results from future studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".