Masticatory muscle reaction in simulated low-velocity rear-end impacts.
Bibliographic record
Abstract
AIMS: To evaluate the electromyographic (EMG) activity of masseter and temporalis muscles in relation to impact awareness, gender, impact magnitude, and kinematics of head movement in simulated low-velocity rear-end impacts. METHODS: Twenty-nine individuals (17 men and 12 women) were subjected in random order to 3 rear-end impacts: 2 unexpected impacts (chair accelerations of 4.5 m/s2 and 10.1 m/s2) and 1 expected impact (chair acceleration of 10.1 m/s2). The EMG activity of the deep and superficial masseter muscle was recorded bilaterally. EMG activity was also recorded for the left anterior temporalis muscle. Angular acceleration and angular displacement of the head were also recorded. The temporal relationship between onset of the masticatory muscle activity and maximum peak of the kinematics of head movement was determined. RESULTS: The magnitude of normalized masticatory EMG activity ranged from 1.4 to 1.8 times higher (P < .05) for fast unexpected impacts compared to slow unexpected impacts in all masticatory muscles. The magnitude of normalized anterior temporalis EMG peak response ranged from 1.8 to 2.5 times higher (P < .05) in female subjects than in male subjects for all impacts. No significant differences were identified for impact awareness in the magnitude of normalized EMG activity for any masticatory muscle. No significant differences were identified with respect to timing of masticatory muscle response (P > .05). CONCLUSION: EMG activity increased with increased impact magnitude. Temporal and amplitude awareness of a simulated impact did not produce a difference in the masticatory muscle response. Gender differences were identified in the anterior temporalis muscle response. The onset of the masticatory muscle response occurred after peak angular acceleration of the head but prior to peak angular displacement of the head.
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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) | 0.003 | 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".