Turning away from whiplash. An EMG study of head rotation in whiplash impact
Bibliographic record
Abstract
OBJECTIVE: To determine the response of the cervical muscles to whiplash-type perturbations through low-velocity frontal impacts when the head is rotated to the right and left. METHODS: Twenty healthy volunteers were subjected to increasing acceleration in low-velocity frontal impacts, randomly with head rotated either left or right. Bilateral EMG of the sternocleidomastoids, trapezii, and splenii capitis and acceleration of the sled, torso, and head were recorded. RESULTS: With either direction of head rotation at the time of impact, the muscle responses increased with increasing levels of acceleration (p < 0.01). The time to onset and peak electromyogram for all muscles progressively decreased with increasing levels of acceleration. With the head rotated to the left, the left trapezius generated 77% of its maximal voluntary contraction (MVC) EMG (more than double the response of other muscles). In comparison, the right trapezius generated only 33% of its MVC. The right sternocleidomastoid (25%) and left splenius muscles (32%), the ones responsible for head rotation to the left, were more active than their counterparts (the left sternocleidomastoid generated only 5% of its MVC EMG and the right splenius 9%). On the other hand, with the head rotated to the right, the right trapezius generated 71% of its MVC EMG, while the left trapezius generated only 30% of this value. Again, the left sternocleidomastoid (27% of its MVC EMG) and right splenius (28% of its MVC EMG), being responsible for head rotation to the right, were more active than their counterparts (the right sternocleidomastoid generated only 4% of its MVC EMG and the left splenius 13%). CONCLUSIONS: Frontal impacts tend to generate the most muscle activity in the ipsilateral trapezius muscle, increasing the risk of their injury.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".