A Novel Porcine Model of Traumatic Thoracic Spinal Cord Injury
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Spinal cord injury (SCI) researchers have predominately utilized rodents and mice for in vivo SCI modeling and experimentation. From these small animal models have come many insights into the biology of SCI, and a growing number of novel treatments that promote behavioral recovery. It has, however, been difficult to demonstrate the efficacy of such treatments in human clinical trials. A large animal SCI model that is an intermediary between rodent and human SCI may be a valuable translational research resource for pre-clinically evaluating novel therapies, prior to embarking upon lengthy and expensive clinical trials. Here, we describe the development of such a large animal model. A thoracic spinal cord injury at T10/11 was induced in Yucatan miniature pigs (20-25 kg) using a weight drop device. Varying degrees of injury severity were induced by altering the height of the weight drop (5, 10, 20, 30, 40, and 50 cm). Behavioral recovery over 12 weeks was measured using a newly developed Porcine Thoracic Injury Behavior Scale (PTIBS). This scale distinguished locomotor recovery among animals of different injury severities, with strong intra-observer and inter-observer reliability. Histological analysis of the spinal cords 12 weeks post-injury revealed that animals with the more biomechanically severe injuries had less spared white matter and gray matter and less neurofilament immunoreactivity. Additionally, the PTIBS scores correlated strongly with the extent of tissue sparing through the epicenter of injury. This large animal model of SCI may represent a useful intermediary in the testing of novel pharmacological treatments and cell transplantation strategies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it