Effect of Mouthguards on the Transmission of Force Across the Human Jaw
Bibliographic record
Abstract
OBJECTIVE: To determine if impact responses (stiffness and damping values) of the jaw and neck are significantly different between mouthguards of varying material and manufacturing properties, in controlled conditions involving specified upper limb orientation and applied loading. DESIGN: Quasi-experimental design. SETTING: Simon Fraser University biomechanical laboratory. PARTICIPANTS: Twelve male subjects between the ages of 19 to 28 participated in this study. All subjects were healthy, had no missing teeth, and had no mechanical (orthodontic) appliances. MAIN OUTCOME MEASUREMENTS: For each condition, the system stiffness and damping values were estimated using the free vibration technique. Stiffness and damping values are relevant because they are the major determinants of impact force and, potentially, injury risk. It is the first study to show experimentally the quantitative effects of mouthguard in situ. RESULTS: All the mouthguards lowered the system stiffness as compared with the no mouthguard condition (P < 0.001). There was no observed effect on stiffness between the 2 limb orientation positions. Excitation weight had an unexpected effect on system stiffness (P = 0.041), with increasing weight leading to increased stiffness. CONCLUSIONS: The findings suggest that wearing any mouthguard is better than wearing none at all due to their ability to reduce system stiffness and damping values after a blow to the chin.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".