Multilevel Validation of a Male Neck Finite Element Model With Active Musculature
Bibliographic record
Abstract
Computational models of the human neck have been developed to assess human response in impact scenarios; however, the assessment and validation of such models is often limited to a small number of experimental data sets despite being used to evaluate the efficacy of safety systems and potential for injury risk in motor vehicle collisions. In this study, a full neck model (NM) with active musculature was developed from previously validated motion segment models of the cervical spine. Tissue mechanical properties were implemented from experimental studies, and were not calibrated. The neck model was assessed with experimental studies at three levels of increasing complexity: ligamentous cervical spine in axial rotation, axial tension, frontal impact, and rear impact; postmortem human subject (PMHS) rear sled impact; and human volunteer frontal and lateral sled tests using an open-loop muscle control strategy. The neck model demonstrated good correlation with the experiments ranging from quasi-static to dynamic, assessed using kinematics, kinetics, and tissue-level response. The contributions of soft tissues, neck curvature, and muscle activation were associated with higher stiffness neck response, particularly for low severity frontal impact. Experiments presenting single-value data limited assessment of the model, while complete load history data and cross-correlation enabled improved evaluation of the model over the full loading history. Tissue-level metrics demonstrated higher variability and therefore lower correlation relative to gross kinematics, and also demonstrated a dependence on the local tissue geometry. Thus, it is critical to assess models at the gross kinematic and the tissue levels.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".