Stress Concentration of Hybrid Occlusal Splint-Mouthguard during a Simulated Maxillofacial Traumatic Impact: 3D-FEA
Bibliographic record
Abstract
Mouthguards (MG) are protective devices that can reduce the risks of facial trauma. However, many athletes do not use them. Additionally, MG wear with coincidental parafunctional activity has not been considered. The aim of this study was to evaluate the stress distribution as a consequence of a direct impact comparing a conventional MG with a novel hybrid appliance (HMG). Using computer-aided design (CAD) software, a human skull was modeled with the teeth inserted into their respective alveolus. The models were divided according to the MG type (conventional or hybrid). The geometries were exported to the computer-aided engineering (CAE) software and the materials were considered isotropic. Fixation was defined at the base of the maxilla. The load was applied using a hockey puck. The total deformation (mm) and the von Mises stress (MPa) results were obtained for the MGs (conventional and hybrid), upper teeth, lower teeth, and maxillary bone. Despite the presence of an MG, it is still possible to observe generated stress in all structures. However, the hybrid design was more efficient than the conventional design in reducing the displacement during the impact and consequently the stress on the upper teeth, lower teeth, and maxillary bone. Higher stress magnitude was more concentrated at the inner portion of the hybrid design than the conventional device. The HMG appliance decreased the stress concentration in the teeth and in the bone, limiting the areas susceptible to injuries to the regions directly impacted by the hockey puck. Although the novel HMG may mitigate injury, some stress will still result, and any possible injury should be evaluated by a dental professional.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".