Effect of facet inclination and location on TMJ loading during bruxism: An in-silico study
Bibliographic record
Abstract
Functional impairment of the masticatory region can have significant consequences that range from a loss of quality of life to severe health issues. Increased temporomandibular joint loading is often connected with temporomandibular disorders, but the effect of morphological factors on joint loading is a heavily discussed topic. Due to the small size and complex structure of the masticatory region in vivo investigations of these connections are difficult to perform. We propose a novel in silico approach for the investigation of the effect of wear facet inclination and position on TMJ stress. We use a forward-dynamics tracking approach to simulate lateral bruxing on the canine and first molar using 6 different inclinations, resulting in a total of 12 simulated cases. By using a computational model, we control a single variable without interfering with the system. Muscle activation pattern, maximum bruxing force as well as TMJ disc stress are reported for all simulations. Muscle activation patterns and bruxing forces agree well with previously reported EMG findings and in vivo force measurements. The simulation results show that an increase in inclination leads to a decrease in TMJ loading. Wear facet position seems to play a smaller role with regard to bruxing force but might be more relevant for TMJ loading. Together these results suggest a possible effect of tooth morphology on TMJ loading during bruxism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".