The effect of periapical bone defects on stress distribution in teeth with periapical periodontitis: a finite element analysis
Bibliographic record
Abstract
BACKGROUND: Apical periodontitis directly affects the stress state of the affected tooth owing to the destruction of the periapical bone. Understanding the mechanical of periapical bone defects/tooth is clinically meaningful. In this study, we evaluate the effect of periapical bone defects on the stress distribution in teeth with periapical periodontitis using finite element analysis. METHODS: Finite element models of normal mandibular second premolars and those with periapical bone defects (spherical defects with diameters of 5, 10, 15, and 20 mm) were created using a digital model design software. The edges of the mandible were fixed and the masticatory cycle was simplified as oblique loading (a 400 N force loaded obliquely at 45° to the long axis of the tooth body) to simulate the tooth stress state in occlusion and analyze the von Mises stress distribution and tooth displacement distribution in each model. RESULTS: Overall analysis of the models: Compared to that in the normal model, the maximum von Mises stresses in all the different periapical bone defect size models were slightly lower. In contrast, the maximum tooth displacement in the periapical bone defect model increased as the size of the periapical bone defect increased (2.11-120.1% of increase). Internal analysis of tooth: As the size of the periapical bone defect increased, the maximum von Mises stress in the coronal cervix of the tooth gradually increased (2.23-37.22% of increase). while the von Mises stress in the root apical region of the tooth showed a decreasing trend (41.48-99.70% of decrease). The maximum tooth displacement in all parts of the tooth showed an increasing trend as the size of the periapical bone defect increased. CONCLUSIONS: The presence of periapical bone defects was found to significantly affect the biomechanical response of the tooth, the effects of which became more pronounced as the size of the bone defect increased.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".