Finite element analysis of the human mandible to assess the effect of removing an impacted third molar.
Bibliographic record
Abstract
BACKGROUND AND AIM: Finite element analysis (FEA) was used to generate 3-dimensional models of a human mandible with impacted third molars. The aim was to analyze the effects of removing various amounts of bone around an impacted mandibular third molar and to predict the possibility of iatrogenic fracture. MATERIALS AND METHODS: Data were acquired from cone beam computed tomography (CBCT) scans of a patient using numerically calculated mechanical parameters. Virtual surgery was then performed on the mandibular models, and standardized chewing forces were applied to the resulting simulations. RESULTS: The modelling showed that the highest stress during normal clenching occurred if the surgical procedure involved the external oblique ridge. The peak stress occurred at the site of removal of the third molar, during contralateral loading of the mandible. DISCUSSION: Use of CBCT allowed production of high-quality models of an individual patient and simulation of various surgical scenarios. FEA identified the accumulation of stress and strain at specific parts of the mandible and predicted the responses of bone to mechanical activity. FEA could prove useful to dental practitioners in the future to predict the likelihood of iatrogenic fracture of the jaws after surgical removal of mandibular bone, such as occurs when the third molar is removed. This may allow dentists to change their approach to tooth removal in certain cases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".