Effect of muscle pre‐tension and pre‐impact neck posture on the kinematic response of the cervical spine in simulated low‐speed rear impacts
Bibliographic record
Abstract
Computational human body models (HBMs) can identify potential injury pathways not easily accessible through experimental studies, such as whiplash induced injuries. However, previous computational studies investigating neck response to simulated impact conditions have neglected the effect of pre-impact neck posture and muscle pre-tension on the intervertebral kinematics and tissue-level response. The purpose of the present study was addressing this knowledge gap using a detailed neck model subjected to simulated low-acceleration rear impact conditions, towards improved intervertebral kinematics and soft tissue response for injury assessment. An improved muscle path implementation in the model enabled the modeling of muscle pre-tension using experimental muscle pre-stretch data determined from previous cadaver studies. Cadaveric neck impact tests and human volunteer tests with the corresponding cervical spine posture were simulated using a detailed neck model with the reported boundary conditions and no muscle activation. Computed intervertebral kinematics of the model with pre-tension achieved, for the first time, the S-shape behavior of the neck observed in low severity rear impacts of both cadaver and volunteer studies. The maximum first principal strain in the muscles for the model with pre-tension was 27% higher than that without pre-tension. Although, the pre-impact neck posture was updated to match the average posture reported in the experimental tests, the change in posture was generally small with only small changes in vertebral kinematics and muscle strain. This study provides a method to incorporate muscle pre-tension in HBM and quantifies the importance of pre-tension in calculating tissue-level distractions.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".