Evaluation of Glazing and Polishing Systems for Novel Chairside CAD/CAM Lithium Disilicate and Virgilite Crowns
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to evaluate the effectiveness of glazing, two zirconia, and two lithium disilicate polishing systems on surface roughness of a CAD/CAM lithium disilicate and virgilite ceramic with atomic force microscopy (AFM) and visual assessment performed by dental students and faculty. METHODS AND MATERIALS: Sixty maxillary right central incisor crowns made of a novel chairside CAD/CAM lithium disilicate and virgilite (CEREC Tessera) were milled for glazing and polishing. The crowns were divided into six groups: no polishing/glazing provided (NoP/G); glazed (GZ); glazed and polished with Brasseler Dialite LD Lithium Disilicate (DiLD); glazed and polished with Meisinger Luster Lithium Disilicate (LuLD); glazed and polished with Brasseler Dialite ZR Zirconia (DiZR); and glazed and polished with Meisinger Luster Zirconia (LuZR). Surfaces were scanned with AFM to measure roughness (Ra) and root mean square roughness (Rq) and generate micrographs. Crowns were visually assessed by 10 dental students and 10 dental school faculty members to determine clinical acceptableness. RESULTS: Glazing and all polishing kits significantly reduced Ra and Rq compared to no polishing/glazing. No significant Ra differences were found between glazing and all polishing kits (p>0.05). DiZR significantly reduced Rq compared to other groups (p<0.05). Visual assessment showed that GZ, LuLD, and DiZR were the most clinically acceptable crowns. CONCLUSION: Polishing and glazing considerably improve the surface smoothness of maxillary central incisor crowns fabricated out of a chairside CAD/CAM lithium disilicate and virgilite ceramic. Altogether, zirconia polishing systems provided smoother and more clinically acceptable surfaces than the lithium disilicate kits.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".