Load-Sharing and Kinematics of the Human Cervical Spine Under Multi-Axial Transverse Shear Loading: Combined Experimental and Computational Investigation
Bibliographic record
Abstract
The cervical spine experiences shear forces during everyday activities and injurious events yet there is a paucity of biomechanical data characterizing the cervical spine under shear loading. This study aimed to (1) characterize load transmission paths and kinematics of the subaxial cervical spine under shear loading, and (2) assess a contemporary finite element cervical spine model using this data. Subaxial functional spinal units (FSUs) were subjected to anterior, posterior, and lateral shear forces (200 N) applied with and without superimposed axial compression preload (200 N) while monitoring spine kinematics. Load transmission paths were identified using strain gauges on the anterior vertebral body and lateral masses and a disc pressure sensor. Experimental conditions were simulated with cervical spine finite element model FSUs (GHBMC M50 version 5.0). The mean kinematics, vertebral strains, and disc pressures were compared to experimental results. The shear force-displacement response typically demonstrated a toe region followed by a linear response, with higher stiffness in anterior shear relative to lateral and posterior shear. Compressive axial preload decreased posterior and lateral shear stiffness and increased initial anterior shear stiffness. Load transmission patterns and kinematics suggest the facet joints play a key role in limiting anterior shear while the disc governs motion in posterior shear. The main cervical spine shear responses and trends are faithfully predicted by the GHBMC cervical spine model. These basic cervical spine biomechanics and the computational model can provide insight into mechanisms for facet dislocation in high severity impacts, and tissue distraction in low severity impacts.
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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 | 0.000 | 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 | 0.000 |
| 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".