An in silico investigation of the effect of bolus properties on TMJ loading during mastication
Bibliographic record
Abstract
Mastication is the motor task with the highest muscle activations of the jaw region, potentially leading to high temporomandibular joint (TMJ) loading. Since increased loading of the TMJ is associated with temporomandibular disorders (TMD), TMJ mechanics during chewing has potential clinical relevance in TMD treatment. TMD self-management guidelines suggest eating soft and small pieces of food to reduce TMJ pain. Since TMJ loading cannot be measured in vivo, due to patient safety restrictions, computer modeling is an important tool for investigations of the potential connection between TMJ loading and TMD. The objective of this study is to investigate the effect of food bolus variables on mechanical TMJ loading to help inform better self-management guidelines for TMD. A combined rigid-body-finite-element model of the jaw region was used to investigate the effect of bolus size, stiffness, and position. Mandibular motion and TMJ disc von Mises stress were reported. Computed mandibular motion generally agrees well with previous literature. Disc stress was higher during the closing phase of the chewing cycle and for the non-working side disc. Smaller and softer food boluses overall lead to less TMJ loading. The results reinforce current guidelines regarding bolus modifications and provide new potential guidelines for bolus positioning that could be verified through a future clinical trial. The paper presents a first in silico investigation of dynamic chewing with detailed TMJ stress for different bolus properties. The results help to strengthen the confidence in TMD self-management recommendations, potentially reducing pain levels of patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".