Effects of Ceramic Shade, Ceramic Thickness, and Surface Treatment on the Color Match of High‐Translucency Monolithic Multilayer Zirconia Restorations
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Bibliographic record
Abstract
Purpose: The purpose of this in vitro study was to evaluate the effects of ceramic shade, ceramic thickness, and surface treatment on the color match of high‐translucency monolithic multilayer zirconia restorations. Materials and Methods: Seventy‐two high‐translucency monolithic multilayer zirconia disk specimens with different shades (A2, A3, B2, and B3) and different thicknesses (1, 1.5, and 2 mm) were fabricated, polished, and glazed. CIELab values were measured with a spectrophotometer in the incisal, middle, and cervical regions before and after glazing. ∆ E 00 color differences were calculated between polished and glazed specimens (Δ E 1 ), between polished specimens and their analogous Vita classical shade tabs as targets (Δ E 2 ), and between glazed specimens and the targets (Δ E 3 ). The ∆ E 00 values were compared with a 50:50% acceptability threshold (∆ E 00 = 1.8) to assess color matches. Repeated measures ANOVA, Bonferroni, and 1‐sample t ‐tests were used for data analysis ( α = 0.05). Results: Mean ∆ E 00 values ranged between 1.11 and 2.74 for Δ E 1 , between 2.69 and 6.78 for Δ E 2 , and between 1.47 and 5.59 for Δ E 3 . The overall mean values were 1.82, 4.66, and 3.63 for Δ E 1 , Δ E 2 , and Δ E 3 , respectively. Ceramic shade, ceramic thickness, and surface treatment significantly affected the CIELab values ( p < 0.05). All mean ∆ E 2 and ∆ E 3 values were greater than the threshold ( p < 0.05) except for the mean ∆ E 3 for the 1.5‐ and 2‐mm‐thick, A3 shade, glazed zirconia in the cervical region ( p > 0.05). Conclusions: The color match of high‐translucency monolithic multilayer zirconia restorations depends on ceramic shade. Additionally, increased ceramic thicknesses (≥1.5 mm) and glazing can improve the color match of these restorations.
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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 | 0.000 |
| 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) | 0.000 | 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 it