A Unilateral Cervical Spinal Cord Contusion Injury Model in Non-Human Primates ( <i>Macaca mulatta</i> )
Bibliographic record
Abstract
The development of a non-human primate (NHP) model of spinal cord injury (SCI) based on mechanical and computational modeling is described. We scaled up from a rodent model to a larger primate model using a highly controllable, friction-free, electronically-driven actuator to generate unilateral C6-C7 spinal cord injuries. Graded contusion lesions with varying degrees of functional recovery, depending upon pre-set impact parameters, were produced in nine NHPs. Protocols and pre-operative magnetic resonance imaging (MRI) were used to optimize the predictability of outcomes by matching impact protocols to the size of each animal's spinal canal, cord, and cerebrospinal fluid space. Post-operative MRI confirmed lesion placement and provided information on lesion volume and spread for comparison with histological measures. We evaluated the relationships between impact parameters, lesion measures, and behavioral outcomes, and confirmed that these relationships were consistent with our previous studies in the rat. In addition to providing multiple univariate outcome measures, we also developed an integrated outcome metric describing the multivariate cervical SCI syndrome. Impacts at the higher ranges of peak force produced highly lateralized and enduring deficits in multiple measures of forelimb and hand function, while lower energy impacts produced early weakness followed by substantial recovery but enduring deficits in fine digital control (e.g., pincer grasp). This model provides a clinically relevant system in which to evaluate the safety and, potentially, the efficacy of candidate translational therapies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".