In-vivo Kinematics of the Cervical Spine in Frontal Sled Tests
Bibliographic record
Abstract
The description of cervical spine motion and the risk to sustain a cervical spine injury in traffic accidents is mainly based on rear-end collisions. The knowledge about frontal collisions is comparable low. Therefore the objective of this exploratory study was, to describe the in-vivo cervical spine motion and acceleration during simulated frontal sled collisions and to identify sequences of motion in which the risk of injury is increased. A frontal collision with a speed change of 10.2km/h was simulated in a sled test with ten volunteers. Cervical spine kinematics was assessed by the simultaneous analysis of the angular head motion and acceleration as well as the simultaneous analysis of the relative motion and acceleration between the head and the first thoracic vertebral body. The motion sequence was divided into five phases. The combination of peak values of the angular head acceleration to ventral and the relative horizontal head acceleration to dorsal between the time period of 90ms and 110ms (early flexion phase) included - potential injury generating - shear forces. Although a hyperflexion (late rebound phase) as injury pattern didn't occur, dorsal soft tissue injuries due to eccentric muscle-sprain could not be ruled out completely. In conclusion the study showed under simulated test conditions that during the early flexion phase and the late rebound phase, acceleration and movement pattern occur that could lead to cervical spine injuries.
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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 | 0.000 | 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".