Artificial Enamel Wear after Prolonged Chewing Simulation against Monolithic Y-TZP Crowns
Bibliographic record
Abstract
The aim of this study was to evaluate the effect of chewing simulation on wear of artificial enamel abraded against zirconia-based crowns. Fifteen crown preparations were scanned for the manufacturing of crowns using computer-aided-design/computer-aided-machining technique (CAD/CAM), according to the following (n = 5): Polished (PM) and glazed (GM) monolithic zirconia (1.5 mm uniform thickness), and Bilayer (BL - 0.8 mm zirconia coping, 0.7 mm porcelain veneer) crowns. The samples were cemented and chewing simulation (2.5 million cycles/0-80N/artificial saliva/37°C) was performed with steatite indenters (6 mm diameter) as antagonists. Assuming the uniformity of the unaged samples, antagonists were scanned using a surface profilometer and the material loss volume was calculated. Roughness of the crowns’ occlusal surface was also analyzed using the profilometer. Scanning electron microscopy was used to characterize the abraded surface. One-way ANOVA and Tukey test (p = 0.05) were employed for analysis of wear results. A significant difference was observed among the groups (p 3 ± 0.015) than those abraded against monolithic zirconia, polished (PM - 0.167 mm3 ± 0.02) and glazed (0.101 mm3 ± 0.03), which were similar to each other. Veneering porcelain results in more pronounced wear of the artificial enamel than monolithic zirconia. However, mastication against monolithic Y-TZP also imposes wear to the opposing teeth.
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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 | 0.000 |
| 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 | 0.000 | 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".