Applications of Finite Element Analysis in Endodontics: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Endodontics increasingly uses finite element analysis (FEA) to evaluate stress distribution, fracture resistance, and temperature changes in treated teeth. FEA's endodontic uses, benefits, and drawbacks are examined in this systematic review and meta-analysis. Methods: A PubMed systematic search found relevant studies published up to January 2022. Original endodontic research articles utilizing FEA to quantify stress distribution, fracture resistance, or temperature changes in treated teeth were eligible. The systematic review comprised 30 publications, 15 of which were meta-analyzed. Data were extracted using a standard form, and the "Newcastle-Ottawa Scale (NOS)" for observational studies and the Cochrane risk of bias tool for randomized controlled trials assessed quality. Random-effects models calculated pooled effect sizes and 95% confidence intervals in RevMan 5.4 meta-analysis. Results: = 0.12). Stress distribution had an effect size of 0.75 (95% CI: 0.65-0.85), fracture resistance 0.42 (95%: 0.12-0.72), and temperature variations -0.18. Conclusion: In conclusion, FEA is a valuable technique in endodontics for stress distribution study and fracture resistance testing. FEA models' accuracy, dependability, and clinical applicability were questioned, underlining the need for more research and development to maximize their endodontics clinical use.
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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 | 0.027 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.037 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".