The effect of tooth cusp morphology and grinding direction on TMJ loading during bruxism
Bibliographic record
Abstract
Increased mechanical loading of the temporomandibular joint (TMJ) is often connected with the onset and progression of temporomandibular joint disorders (TMD). The potential role of occlusal factors and sleep bruxism in the onset of TMD are a highly debated topic in literature, but ethical considerations limit in vivo examinations of this problem. The study aims to use an innovative in silico modeling approach to thoroughly investigate the connection between morphological parameters, bruxing direction and TMJ stress. A forward-dynamics tracking approach was used to simulate laterotrusive and mediotrusive tooth grinding for 3 tooth positions, 5 lateral inclination angles, 5 sagittal tilt angles and 3 force levels, giving a total of 450 simulations. Muscle activation patterns, TMJ disc von Mises stress as well as correlations between mean muscle activations and TMJ disc stress are reported. Computed muscle activation patterns agree well with previous literature. The results suggest that tooth inclination and grinding position, to a smaller degree, have an effect on TMJ loading. Mediotrusive bruxing computed higher loads compared to laterotrusive simulations. The strongest correlation was found for TMJ stress and mean activation of the superficial masseter. Overall, our results provide in silico evidence that TMJ disc stress is related to tooth morphology.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".