Stress distributions in maxillary central incisors restored with various types of post materials and designs
Bibliographic record
Abstract
Different dental post designs and materials affect the stability of restoration of a tooth. This study aimed to analyse and compare the stability of two shapes of dental posts (parallel-sided and tapered) made of five different materials (titanium, zirconia, carbon fibre and glass fibre) by investigating their stress transfer through the finite element (FE) method. Ten three-dimensional (3D) FE models of a maxillary central incisor restored with two different designs and five different materials were constructed. An oblique loading of 100 N was applied to each 3D model. Analyses along the centre of the post, the crown-cement/core and the post-cement/dentine interfaces were computed, and the means were calculated. One-way ANOVAs followed by post hoc tests were used to evaluate the effectiveness of the post materials and designs (p=0.05). For post designs, the tapered posts introduced significantly higher stress compared with the parallel-sided post (p<0.05), especially along the centre of the post. Of the materials, the highest level of stress was found for stainless steel, followed by zirconia, titanium, glass fibre and carbon fibre posts (p<0.05). The carbon and glass fibre posts reduced the stress distribution at the middle and apical part of the posts compared with the stainless steel, zirconia and titanium posts. The opposite results were observed at the crown-cement/core interface.
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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 | 0.000 |
| 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) | 0.001 | 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".