A METHOD TO GENERATE DIGITAL POPULATIONS FOR FINITE ELEMENT ANALYSIS OF SPINAL CORD INJURY FROM MRIS
Bibliographic record
Abstract
Spinal cord injuries (SCIs) are a devastating outcome following a mechanical impact to the spine, which translates into the constituent tissues of the spinal cord. Since the biomechanics of human SCIs are often unknown, representative animal models are used to study the injury, and computational models are used to study the injury biomechanics. Morphological variability is a key influencing factor in SCI outcomes and is inherently captured in both human SCI incidents and animal experiments, but computational models of SCI often investigate findings in a single geometry and thus, morphological variability is neglected. In this study, we present an approach to incorporate morphological differences into a computational model of a unilateral contusion injury in nonhuman primates (NHP). Pre-injury MRIs of subjects [Formula: see text] were parameterized using four parameters, including the diameters of the cord and canal in both the mediolateral and anterior-posterior axes and inputted into a Python code to generate new geometries within the measured range. The code was used to generate a digital population [Formula: see text] and their average morphology and variability matched that seen in the real NHP subjects. A subset of the geometries [Formula: see text] was used to simulate a unilateral contusion, and the predicted peak forces [Formula: see text] were close to values seen in NHP experiments. Morphological variability alone accounted for a 10% variability in the peak forces experienced by the cord. This study highlights the importance of using multiple geometries in computational models to ensure findings are more robust and clinically translatable.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".