Cervical spine response in frontal crash
Bibliographic record
Abstract
Predicting neck response and injury resulting from motor vehicle accidents is essential to improving occupant protection. A detailed human cervical spine finite element model has been developed, with material properties and geometry determined a priori of any validation, for the evaluation of global kinematics and tissue-level response. Model validation was based on flexion/extension response at the segment level, tension response of the whole ligamentous cervical spine, head kinematic response from volunteer frontal impacts, and soft tissue response from cadaveric whole cervical spine frontal impacts. The validation responses were rated as 0.79, assessed using advanced cross-correlation analysis, indicating the model exhibits good biofidelity. The model was then used to evaluate soft tissue response in frontal impact scenarios ranging from 8G to 22G in severity. Disc strains were highest in the C4-C5-C6 segments, and ligament strains were greatest in the ISL and LF ligaments. Both ligament and disc fiber strain levels exceeded the failure tolerances in the 22G case, in agreement with existing data. This study demonstrated that a cervical spine model can be developed at the tissue level and provide accurate biofidelic kinematic and local tissue response, leading to injury prediction in automotive crash scenarios.
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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) | 0.002 | 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".