Mandibular kinematics associated with simulated low‐velocity rear‐end impacts
Bibliographic record
Abstract
Rear-end-impact motor vehicle accidents may result in cervical and temporomandibular-related pain complaints. Head kimematics in simulated low-impact rear-end impacts have been investigated but mandibular kinematics have not been described. Thirty healthy adult subjects underwent three impacts (4.5 m s(-2) expected, 10.0 m s(-2) unexpected, and 10.0 m s(-2) expected). Onset time and peak magnitude of angular head acceleration, angular mandibular acceleration and angular mandibular displacement were measured. Significant mandibular opening acceleration was not identified with rearward head rotation. The peak magnitude of mandibular closing angular acceleration approximately doubled with increased impact magnitude. No differences in peak angular mandibular acceleration regarding expectation were identified. Gender differences were detected in the fast unexpected impact. The peak time for the angular mandibular acceleration (mandibular closure) was approximately 84-120 ms later than peak rearward angular head acceleration for all impacts. Onset and peak times for angular mandibular acceleration (mandibular closure) were similar to the onset and peak times for forward head acceleration. There was also a positive correlation between the magnitude of the forward angular acceleration of the head and angular acceleration of the mandible for the slow (0.65, P = 0.015) and fast expected (0.844, P = 0.001) impacts. The average angular mandibular angular displacement (mandibular closure) was approximately 6 degrees . The hyperextension hypothesis regarding mechanism of temporomandibular joint injury in low-impact rear-end collisions cannot be supported.
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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 | 0.001 |
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".