A finite element modeling of the human lumbar unit including the spinal cord
Bibliographic record
Abstract
The purpose of this present work is to provide a tool to better understand mechanically related pathologies of the lumbar unit and the spinal structure by providing spinal cord deformations in different loading cases. In fact, spinal cord injury (SCI) resulting from a traumatic movement leades to a deformation of the neural and vascular structure of the spinal cord. And since the magnitude of the spinal cord stress is correlated with the pressure of the vertebral elements, stresses will be computed on all theses components. Physical properties of the vertebrae, various ligaments, the discs, and the spinal cord are described under simple loading as compression, and combined loading, flexion and lateral bending to evaluate the pressure undergone by different components of the lumbar unit. A nonlinear three-dimensional finite element method is used as a numerical tool to perform all the computations. This study provides accurate results for the localisation and the magnitude of maximum equivalent stress and shear stress on the lumbar unit and especially for the spinal cord. These results showed that stresses are more important when a compression of 500 N is combined with a flexion and a lateral bending. In particular, shear stresses are maximum for the spinal cord and the four intervertebral discs for the case of a flexion of 3.8 N.m and a lateral bending of 6.5 N.m.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".