Effect of trunk flexion on cervical muscle EMG to rear impacts
Bibliographic record
Abstract
OBJECTIVE: To determine the effect of occupant positioning on the response of the cervical muscles to whiplash-type posterolateral impacts. METHODS: Twenty healthy volunteers underwent left posterolateral whiplash-type impacts with the volunteers seated "out-of-position". Electromyograms of the cervical muscles were recorded. RESULTS: Whether having the trunk flexed to the left or right at the time of impact, the muscle responses were low in magnitude, showing a trend to increasing EMG responses with increasing acceleration (P>0.05). The time to onset and time to peak electromyogram for most muscles showed a trend to progressively decrease with increasing levels of acceleration. With the subject flexed to the left, all muscles generated 31% or less of the maximal voluntary contraction electromyogram. With the subject flexed to the right, all muscles generated 27% or less of their maximal electromyogram. In both positions, the trapezii were the most active (P<0.05). Thus, having the trunk flexed out of neutral posture at the time of impact produces a very low magnitude cervical muscle response compared to impacts with the trunk in neutral posture. CONCLUSIONS: In the absence of bodily impact, the flexed trunk posture appears to produce a biomechanical response that would probably decrease the likelihood of cervical muscle injury in low velocity posterolateral impacts.
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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 | 0.002 |
| 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) | 0.004 | 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".