The effect of root canal treatment and post-crown restorations on stress distribution in teeth with periapical periodontitis: a finite element analysis
Bibliographic record
Abstract
AIM: To evaluate the effects of root canal treatment (RCT) and post-crown restoration on stress distribution in teeth with periapical bone defects using finite element analysis. METHODOLOGY: Finite element models of mandibular second premolars and those with periapical bone defects (spherical defects with diameters of 5, 10, 15, and 20 mm) were created using digital model design software. The corresponding RCT and post-crown restoration models were constructed based on the different sizes of periapical bone defect models. The von Mises stress and tooth displacement distributions were comprehensively analyzed in each model. RESULTS: Overall analysis of the models: RCT significantly increased the maximum von Mises stresses in teeth with periapical bone defects, while post-crown restoration greatly reduced the maximum von Mises stresses. RCT and post-crown restoration slightly reduced tooth displacement in the affected tooth. Internal analysis of tooth: RCT dramatically increased the maximum von Mises stress in all regions of the tooth, with the most pronounced increase in the coronal surface region. The post-crown restoration balances the internal stresses of the tooth and is most effective in periapical bone defect - 20-mm model. RCT and post-crown restoration slightly reduced the tooth displacement in all regions of the affected tooth. CONCLUSIONS: Root canal treatment seemed not to improve the biomechanical state of teeth with periapical bone defects. In contrast, post-crown restoration might effectively balance the stress concentrations caused by periapical bone defects, particularly extensive ones.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".