Adaptation and Biomechanical Performance of Custom‐Fit Mouthguards Produced Using Conventional and Digital Workflows: A Comparative In Vitro Strain Analysis
Bibliographic record
Abstract
ABSTRACT Background/Objectives The use of different models for the fabrication of custom‐fit mouthguards (MTGs) can affect their final thickness, adaptation, and shock‐absorption properties. This study aimed to evaluate the adaptation, thickness, and shock absorption of ethylene‐vinyl acetate (EVA) thermoplastic MTGs produced using conventional plaster or three‐dimensional (3D) printed models. Materials and Methods A typical model with simulated soft gum tissue was used as the reference model to produce MTGs with the following two different protocols: plast‐MTG using a conventional impression and plaster model ( n = 10) and 3DPr‐MTG using a digital scanning and 3D printed model ( n = 10). A custom‐fit MTG was fabricated using EVA sheets (Bioart) plasticized over different models. The MTG thickness (mm), internal adaptation (mm) to the typodontic model, and voids in the area (mm 2 ) between the two EVA layers were measured using cone‐beam computed tomography images and Mimics software (Materialize). The shock absorption of the MTG was measured using a strain‐gauge test with a pendulum impact at 30° with a steel ball over the typodont model with and without MTGs. Data were analyzed using one‐way analysis of variance with repeated measurements, followed by Tukey's post hoc tests. Results The 3DPr‐MTG showed better adaptation than that of the Plast‐MTG at the incisal/occlusal and lingual tooth surfaces ( p < 0.001). The 3DPr‐MTG showed a thickness similar to that of the Plast‐MTG, irrespective of the measured location. MTGs produced using both model types significantly reduced the strain values during horizontal impact (3DPr‐MTG 86.2% and Plast‐MTG 87.0%) compared with the control group without MTG ( p < 0.001). Conclusion The MTGs showed the required standards regarding thickness, adaptation, and biomechanical performance, suggesting that the number and volume of voids had no significant impact on their functionality. Three‐dimensional printed models are a viable alternative for MTG production, providing better adaptation than the Plast‐MTG at the incisal/occlusal and lingual tooth surfaces and similar performance as the MTG produced with the conventional protocol.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".