Enhancing the Biofidelity of an Upper Cervical Spine Finite Element Model Within the Physiologic Range of Motion and Its Effect on the Full Ligamentous Neck Model Response
Bibliographic record
Abstract
Contemporary finite element (FE) neck models are developed in a neutral posture; however, evaluation of injury risk for out-of-position impacts requires neck model repositioning to non-neutral postures, with much of the motion occurring in the upper cervical spine (UCS). Current neck models demonstrate a limitation in predicting the intervertebral motions within the UCS within the range of motion, while recent studies have highlighted the importance of including the tissue strains resulting from repositioning FE neck models to predict injury risk. In the current study, the ligamentous cervical spine from a contemporary neck model (GHBMC M50 v4.5) was evaluated in flexion, extension, and axial rotation by applying moments from 0 to 1.5 N·m in 0.5 N·m increments, as reported in experimental studies and corresponding to the physiologic loading of the UCS. Enhancements to the UCS model were identified, including the C0-C1 joint-space and alar ligament orientation. Following geometric enhancements, an analysis was undertaken to determine the UCS ligament laxities, using a sensitivity study followed by an optimization study. The ligament laxities were optimized to UCS-level experimental data from the literature. The mean percent difference between UCS model response and experimental data improved from 55% to 23% with enhancements. The enhanced UCS model was integrated with a ligamentous cervical spine (LS) model and assessed with independent experimental data. The mean percent difference between the LS model and the experimental data improved from 46% to 35% with the integration of the enhanced UCS model.
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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 | 0.002 | 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 | 0.000 | 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".