Effect of Torso Boundary Conditions on Spine Kinematic and Injury Responses in Head-First Impact Assessed with a 50th Percentile Male Human Body Model
Bibliographic record
Abstract
<div>Computational and experimental studies have been undertaken to investigate injurious head-first impacts (HFI), which can occur during automotive rollovers. Recent studies assume a torso surrogate mass (TSM) boundary condition, wherein the first or first two thoracic vertebrae are potted and constrained to only move in the vertical loading direction. The TSM boundary condition has not been compared with a full body (FB) model computationally or experimentally for HFI. In this study, the Global Human Body Models Consortium 50th percentile male detailed human body model (M50-O, Version 6.0) was applied to compare the kinematic, kinetic, and injury response of an HFI with a TSM boundary condition (M50-TSM), and a full body boundary condition (M50-FB). Impacts (to M50-TSM and M50-FB) were simulated between the head and a rigid plate using a commercial FE code (LS-DYNA). The impact velocity of 3.1 m/s corresponded to the onset of spinal injury in diving reconstructions, and the impact velocity reported in experiments. The TSM boundary condition was simulated by applying a mass of 16 kg to the first thoracic vertebra (T1), and constraining motion to only the vertical direction. A quantitative comparison of the head and spine impact forces, spine kinematics, and prediction of hard tissue fracture was reported. The M50-TSM model demonstrated a 53.4% lower (straighter) spinal curvature 10 ms after impact, compared to the M50-FB. The lower curvature of the M50-TSM resulted in higher neck loads during that timeframe (2.26 kN M50-TSM, 1.44 kN M50-FB). The resulting hard tissue fracture in M50-TSM was attributed to direct compression at an early time (&lt;5 ms) in the impact, while M50-FB demonstrated compression-extension fractures later (&gt;16 ms) in the simulation. It was concluded that kinematics, kinetics, and injury response differed for the TSM and FB boundary conditions, and therefore these conditions are critical to consider when investigating HFI.</div>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".