Gradation of Neck Muscle Responses and Head/Neck Kinematics to Acceleration and Speed Change in Rear-end Collisions
Bibliographic record
Abstract
Recent epidemiological evidence shows that the potential for whiplash injury varies with both the average acceleration and speed change of a rear-end collision. The goal of this study was to examine the gradation of neck muscle responses and the head and neck kinematics to rear-end collision pulses in which the acceleration and speed change were independently varied. Thirty subjects (15F, 15M) underwent 36 consecutive rear-end collisions consisting of three different average accelerations (ā = 0.5, 0.9 and 1.3 g) and three different speed changes (Deltav = 0.25, 0.50 and 0.75 m/s). Onset and amplitude of the sternocleidomastoid (SCM) and cervical paraspinal (PARA) muscle responses were measured using surface electromyography. Kinematic measures included linear and angular accelerations and displacements of the head and torso. The results showed that the amplitude of the muscle and kinematics responses was graded to both collision acceleration and speed change. The magnitude of early peaks in the head/neck kinematics correlated more strongly with collision acceleration (r(2) = 0.63 to 0.69), whereas the magnitude of later kinematic peaks correlated more strongly with collision speed change (r(2) = 0.59 to 0.95). Onset of the SCM muscle response correlated only weakly with collision acceleration and speed change (r(2) </= 0.07). A collision pulse descriptor obtained by multiplying average acceleration and speed change (āDeltav) yielded the strongest and most consistent correlations with neck muscle (r(2) = 0.48 to 0.58) and head/neck kinematic responses (r(2) = 0.78 to 0.94). This measure of collision severity is also consistent with the recent epidemiological evidence that whiplash symptom intensity and duration increases with both average acceleration and speed change.
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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 | 0.003 |
| 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 | 0.000 | 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".