Mechanical Design and Analysis of a Unilateral Cervical Spinal Cord Contusion Injury Model in Non-Human Primates
Bibliographic record
Abstract
Non-human primate (NHP) models of spinal cord injury better reflect human injury and provide a better foundation to evaluate potential treatments and functional outcomes. We combined finite element (FE) and surrogate models with impact data derived from in vivo experiments to define the impact mechanics needed to generate a moderate severity unilateral cervical contusion injury in NHPs (Macaca mulatta). Three independent variables (impactor displacement, alignment, and pre-load) were examined to determine their effects on tissue level stresses and strains. Mechanical measures of peak force, peak displacement, peak energy, and tissue stiffness were analyzed as potential determinants of injury severity. Data generated from FE simulations predicted a lateral shift of the spinal cord at high levels of compression (>64%) during impact. Submillimeter changes in mediolateral impactor position over the midline increased peak impact forces (>50%). Surrogate cords established a 0.5 N pre-load protocol for positioning the impactor tip onto the dural surface to define a consistent dorsoventral baseline position before impact, which corresponded with cerebrospinal fluid displacement and entrapment of the spinal cord against the vertebral canal. Based on our simulations, impactor alignment and pre-load were strong contributors to the variable mechanical and functional outcomes observed in in vivo experiments. Peak displacement of 4 mm after a 0.5N pre-load aligned 0.5-1.0 mm over the midline should result in a moderate severity injury; however, the observed peak force and calculated peak energy and tissue stiffness are required to properly characterize the severity and variability of in vivo NHP contusion injuries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".