Modeling of muscle forces in humans with and without temporomandibular joint disorders
Bibliographic record
Abstract
OBJECTIVES: Subjects with/without temporomandibular joint disorders (TMJD) were tested for differences in muscle forces. SETTING AND SAMPLE POPULATION: School of Dental Medicine, University at Buffalo. Ninety-one subjects were classified in four groups based on the presence/absence (±) of chronic myofascial and/or TMJ pain (P) and bilateral disc displacement (DD). MATERIAL AND METHODS: Validated numerical models employed an organizational objective and subjects' anatomy to calculate masticatory muscle forces during static biting. anova and Holm's step-down procedure post hoc tests assessed group differences. Theoretical geometries, representing the range of subjects' muscle orientations, were surveyed via numerical models to identify key combinations resulting in high muscle forces. Effect size (Cohen's d) and anova/post hoc tests assessed group differences in key muscle orientations. RESULTS: +P-DD subjects had significantly higher muscle forces, especially for lateral pterygoid muscles, compared to the other groups (p<0.01) for bite forces that were directed posteromedially or posterolaterally on mandibular molars and posteriorly and slightly medially on mandibular incisors. Key muscle orientations for peak lateral pterygoid muscle forces were identified, and group comparisons showed mean orientation in +P-DD compared to other diagnostic groups was ≥5° more upright for masseter and ≥3° more posteriorly directed for temporalis muscles (all Cohen's d≥0.8). CONCLUSION: Predicted lateral pterygoid muscle forces were significantly higher in +P-DD compared to other groups for specific biting conditions and were attributable, in part, to differences in masseter and temporalis muscle orientations.
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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 | 0.004 | 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 | 0.000 | 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".