Smoothed particle hydrodynamics implementation to enhance vertebral fracture finite element model in a cervical spine segment under compression
Bibliographic record
Abstract
Spinal cord injuries (SCIs) can arise from compression loading when a vertebra fractures and bone fragments are pushed into the spinal canal. Experimental studies have demonstrated the importance of both fracture initiation and post-fracture response in the investigation of vertebral fractures and spinal canal occlusion resulting from compression. Finite element models, such as the Global Human Body Models Consortium (GHBMC) model, focused on predicting the initiation location of fractures using element erosion to model hard tissue fracture. However, the element erosion method resulted in a loss of material and structural support during compression, which limited the ability of the model to predict the post-fracture response. The current study aimed to improve the post-fracture response by combining strain-based element erosion with smoothed particle hydrodynamics (SPH) to preserve the volume of the trabecular bone during compression fracture. The proposed implementation was evaluated using a model comprising two functional spinal units (FSUs) (C5-C6-C7) extracted from the GHBMC 50th percentile male model, and loaded under central compression. The original and enhanced models were compared to experimental force-displacement data and measured occlusion of the spinal canal. The enhanced model with SPH improved the shape and magnitude of the force-displacement response to be in good agreement with the experimental data. In contrast to the original model, the enhanced SPH model demonstrated occlusion on the same order of magnitude as reported in the experiments. The SPH implementation improved the post-fracture response by representing the damaged material post-fracture, providing structural support throughout compression loading and material flow leading to occlusion.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".