Effect of Impact Parameters on a Unilateral Contusion Model of Spinal Cord Injury in a Virtual Population of Non-Human Primates
Bibliographic record
Abstract
Non-human primate (NHP) spinal cord injury experiments exhibit high intersubject variability in biomechanical parameters even when a consistent impact protocol is applied to each subject. Optimizing impact parameters to reduce this variability through experiments is logistically challenging in NHP studies. Finite element models provide a complimentary tool to inform experimental design without the cost and complexity of live animal studies. A morphologically variable virtual population ( N = 10) of NHPs quantified the interaction of morphological variability and different impact conditions in a unilateral cervical contusion, including impactor size (4 and 5 mm) and mediolateral alignment over the cord midline (0.5 and 1 mm). We explored the effect of these conditions on the magnitude and intersubject variability of impact force and cord lateral slippage. The study demonstrated that a 1-mm mediolateral alignment maximized peak forces and minimized lateral slippage. A 5-mm impactor was beneficial in increasing peak forces, whereas a 4-mm impactor reduced lateral slippage. Comparatively, intersubject variability in peak forces and lateral slippage were minimized with a 0.5-mm mediolateral alignment. The study highlights that impact parameters selected based on peak forces may not be beneficial in reducing variability. The study also showed that morphology was an important contributor to variability. Integrating morphology variability through a virtual population in an injury simulation to investigate mechanistic research questions will more effectively capture the heterogeneity of experiments and provide better insights for effective experimental design.
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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 | 0.000 |
| 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".