Development of a large-animal model to measure dynamic cerebrospinal fluid pressure during spinal cord injury
Bibliographic record
Abstract
OBJECT: Spinal cord injury (SCI) often results in considerable permanent neurological impairment, and unfortunately, the successful translation of effective treatments from laboratory models to human patients is lacking. This may be partially attributed to differences in anatomy, physiology, and scale between humans and rodent models. One potentially important difference between the rodent and human spinal cord is the presence of a significant CSF volume within the intrathecal space around the human cord. While the CSF may "cushion" the spinal cord, pressure waves within the CSF at the time of injury may contribute to the extent and severity of the primary injury. The objective of this study was to develop a model of contusion SCI in a miniature pig and establish the feasibility of measuring spinal CSF pressure during injury. METHODS: A custom weight-drop device was used to apply thoracic contusion SCI to 17 Yucatan miniature pigs. Impact load and velocity were measured. Using fiber optic pressure transducers implanted in the thecal sac, CSF pressures resulting from 2 injury severities (caused by 50-g and 100-g weights released from a 50-cm height) were measured. RESULTS: The median peak impact loads were 54 N and 132 N for the 50-g and 100-g injuries, respectively. At a nominal 100 mm from the injury epicenter, the authors observed a small negative pressure peak (median -4.6 mm Hg [cranial] and -5.8 mm Hg [caudal] for 50 g; -27.6 mm Hg [cranial] and -27.2 mm Hg [caudal] for 100 g) followed by a larger positive pressure peak (median 110.5 mm Hg [cranial] and 77.1 mm Hg [caudal] for 50 g; 88.4 mm Hg [cranial] and 67.2 mm Hg [caudal] for 100 g) relative to the preinjury pressure. There were no significant differences in peak pressure between the 2 injury severities or the caudal and cranial transducer locations. CONCLUSIONS: A new model of contusion SCI was developed to measure spinal CSF pressures during the SCI event. The results suggest that the Yucatan miniature pig is an appropriate model for studying CSF, spinal cord, and dura interactions during injury. With further development and characterization it may be an appropriate in vivo large-animal model of SCI to answer questions regarding pathological changes, therapeutic safety, or treatment efficacy, particularly where humanlike dimensions and physiology are important.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".