Effect of Trunk Flexion on the Occupant Neck Response to Anterolateral Whiplash Impacts
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to determine the response of the cervical muscles to increasing low-velocity anterolateral impacts with the volunteer's trunk flexed to the right and left. METHODS: A total of 20 healthy volunteers were subjected to left anterolateral impacts of 4.0, 7.6, 10.7, and 13.4 m/sec and, sequentially, with trunk flexed either left or right. Bilateral electromyograms (EMGs) of the sternocleidomastoids, trapezii, and splenii capitis were recorded. DESIGN: At an acceleration of 13.4 m/sec, with the trunk flexed left, the left trapezius generated 48% of its maximal voluntary contraction EMG, whereas the right trapezius (contralateral to the left anterolateral impact) generated 38% of this variable. All other muscle generated </=23% of their maximal voluntary contraction EMG, a significant difference from the trapezii (P = 0.005). Similarly, with the trunk flexed to the right under these same conditions, the left trapezius generated 26% and the right trapezius 35% of their maximal voluntary contraction EMG. Again, all other muscles generated significantly less EMG activity, </=22% (P = 0.009). Overall, the EMG responses were of low magnitude compared with known data with the trunk in neutral posture in this direction of impact. CONCLUSIONS: When the subject sits with trunk flexed out of neutral posture at the time of an anterolateral impact, the cervical muscle response is reduced compared with anterolateral impacts with the trunk in neutral posture.
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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) | 0.003 | 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".