Maximum Principal Strain Correlates with Spinal Cord Tissue Damage in Contusion and Dislocation Injuries in the Rat Cervical Spine
Bibliographic record
Abstract
The heterogeneity of the primary mechanical mechanism of spinal cord injury (SCI) is not currently used to tailor treatment strategies because the effects of these distinct patterns of acute mechanical damage on long-term neuropathology have not been fully investigated. A computational model of SCI enables the dynamic analysis of mechanical forces and deformations within the spinal cord tissue that would otherwise not be visible from histological tissue sections. We created a dynamic, three-dimensional finite element (FE) model of the rat cervical spine and simulated contusion and dislocation SCI mechanisms. We investigated the relationship between maximum principal strain and tissue damage, and compared primary injury patterns between mechanisms. The model incorporated the spinal cord white and gray matter, the dura mater, cerebrospinal fluid, spinal ligaments, intervertebral discs, a rigid indenter and vertebrae, and failure criteria for ligaments and vertebral endplates. High-speed (∼ 1 m/sec) contusion and dislocation injuries were simulated between vertebral levels C3 and C6 to match previous animal experiments, and average peak maximum principal strains were calculated for several regions at the injury epicenter and at 1-mm intervals from +5 mm rostral to -5 mm caudal to the lesion. Average peak principal strains were compared to tissue damage measured previously in the same regions via axonal permeability to 10-kD fluorescein-dextran. Linear regression of tissue damage against peak maximum principal strain for pooled data within all white matter regions yielded similar and significant (p<0.0001) correlations for both contusion (R(2)=0.86) and dislocation (R(2)=0.52). The model enhances our understanding of the differences in injury patterns between SCI mechanisms, and provides further evidence for the link between principal strain and tissue damage.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".