Neck Injury Risk in Out-of-Position Rear Impact Scenarios Using a Reference Geometry-Based Head Repositioning
Bibliographic record
Abstract
Rear-end vehicle collisions may lead to whiplash-associated disorders (WADs), comprising a variety of neck and head pain responses. Specifically, increased axial head rotation has been associated with the risk of injuries during rear impacts, while specific tissues, including the capsular ligaments, have been implicated in pain response. Given the limited experimental data for out-of-position rear impact scenarios, computational human body models (HBMs) can inform the potential for tissue-level injury. Previous studies have considered external boundary conditions to reposition the head axially but were limited in reproducing a biofidelic movement. The objectives of this study were to implement a novel head repositioning method to achieve targeted axial rotations and evaluate the tissue-level response for a rear impact condition. The repositioning method used reference geometries to rotate the head to three target positions, showing good correspondence to reported interverbal rotations. Under a 7 g rear impact scenario, the head-turned models were compared with the neutral position and demonstrated increases in the maximum capsular ligament distractions. Increased head rotation was associated with increased ligament distractions. The locations with critical ligament distractions shifted to the lower cervical spine (below C3) and lateral portion of the capsular ligaments for the head-turned position cases. The proposed repositioning method introduced in this study enabled the model to achieve steady head rotations with realistic cervical spine movements, increasing the biofidelity of out-of-position rear impact simulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".