Investigation of Upper Cervical Spine Injury due to Frontal and Rear Impact Loading Using Finite Element Analysis
Bibliographic record
Abstract
Predicting neck response and injury resulting from motor vehicle crashes is essential for improving occupant protection, effective prevention, and in the evaluation and treatment of spinal injuries. Injury mechanism of upper cervical spine due to frontal/rear-end impacts was studied using Finite Element (FE) analyses. A FE model of ligamentous (devoid of muscles) occipito-C3 cervical spine was developed. Time and rate-dependent material laws were used for assessing bone and ligament failure. Frontal and rear-end impact loads at two rates of 5G and 10G accelerations were applied to analyze the model response in terms of stress distribution, intradiscal pressure change, and contact pressure in facet joints. Failure occurrence and initiation instants were investigated. Frontal and rear-end impacts increased stresses significantly producing failure in most components for both rates. However, transverse ligament and C2-vertebral endplate only failed under rear-end impact. No failure occurred in cortical bone, dens, disc, anterior or posterior longitudinal ligaments. The spine is more prone to injury under rear-end impact as most of the spinal components failed and failure started earlier. Ligaments and facet joints are the most vulnerable components of the upper cervical spine when subjected to frontal/rear end impacts and injury may occur at small ranges of displacement/rotation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".