Comparison of Two Intervertebral Disc Failure Models in a Numerical C4-C5 Trauma Model
Bibliographic record
Abstract
The intervertebral disc (IVD) is essential for the mobility and stability of the spine. During flexion-distraction injuries, which are frequent at the cervical spine level, the IVD is often disrupted. Finite element studies have been done to investigate injury mechanisms and patterns at the cervical spine. However, they rarely include IVD failure model. The aim of this paper was to implement and compare two types of IVD failure models and their impact on hyperflexion and hyperflexion-compression injuries simulations. The failure models were tested on a detailed C4-C5 finite elements model. The first failure model consisted in a maximal strain model applied to the elements of the annulus and nucleus. The second failure model consisted in the implementation of a rupture plane in the middle of the IVD with a tied interface created between the two sections. This interface is defined by threshold stress values of detachment in traction and shearing. The two failure models were tested in flexion only and in flexion-compression. The model without inclusion of an IVD failure model was also tested. Loads at failure and injury patterns were reported. Both failure models produce failure loads that were consistent with experimental data. Injury patterns observed were in agreement with experimental and numerical studies. However, in flexion-compression, the rupture plane model simulation reached important energy error due to high deformations in the IVD elements. Also, without inclusion of an IVD failure model, energy error forced the end of the simulation in flexion-compression. Therefore, inclusion of IVD failure model is important since it leads to realistic results, but the maximal strain failure model is recommended.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".