Development and Validation of a Three-Dimensional Finite Element Model of Cervical Spine (C3-C5)
Bibliographic record
Abstract
The objective of this investigation is to develop a detailed, non-linear asymmetric three-dimensional anatomically and mechanically accurate FE model of complete middle cervical spine (C3-C5) using Hypermesh and MSC.Marc software. To achieve this goal, the components of the cervical spine are modeled using 20-noded hexagonal elements. The model includes the intervertebral disc, cortical bone, cancellous bone, endplates, and ligaments. The structure and dimensions of each spinal component are compared with experimentally measured values. In addition, the soil mechanics formulation of MSC.Marc finite element software is applied to model the mechanical behaviour of vertebrae and intervertebral discs as linear isotropic two-phase (biphasic) material. The FE simulation is conducted to investigate compression, flexion\extension and right\Left lateral bending modes. The simulation results are validated and compared closely with the published experimental data and the existing FE models. In general, results show greater flexibility in flexion and less flexibility in extension. The flexion/extension curves are asymmetric with a greater magnitude in flexion than in extension. In addition, the variations of the predicted lateral C4-C5 disc bulge are investigated and the results show that the maximum disc bulge occurs at the C4-C5 anterior location.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".