A Finite Element Modeling and Simulation of Human Temporomandibular Joint with and Without TM Disorders: An Indian Experience
Bibliographic record
Abstract
Temporomandibular joint (TMJ) is anatomically the most intricate joint which connects the lower jaw to the upper jaw and regulates jaw movements. It significantly deals with mastication and speech. It is hence imperative to study the mechanics and functioning of the jaw joint to devise alternative solutions for its replacement whenever required. Further, human skulls are anthropologically categorized into three types – African, Asian and European. Out of these, the Indian skull is also a bit different than its Asian counterparts because of its osteology and skeletal biology. Hence, a comprehensive biomechanical and computational study is essential to provide customized solutions. For the present study, four different loading conditions are selected to perform finite element analysis on the human skull, Anonymized and unidentifiable CT scan data sets from open-source web platforms are converted to STL and then 3D models using 3D slicer. Finite element analysis of jaw joint is carried out. Results based on Von Mises stress studies show significant behavioral differences under varying load conditions. Hence, it is crucial to identify solutions for TMJ disorders of the Indian population.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".