Color and translucency stability of novel restorative CAD/CAM materials
Bibliographic record
Abstract
BACKGROUND: The wide range of restorative materials available for use in the computer-aided design/ computer-aided manufacturing (CAD/CAM) technology requires a better understanding of their esthetic properties. OBJECTIVES: The aim of the study was to assess the stability of the color and translucency of different CAD/ CAM restorative materials before and after being subjected to different staining solutions. MATERIAL AND METHODS: A total of 160 disc-shaped specimens were prepared from glass ceramic (IPS-e.max®-CAD and Celtra Duo®), high-translucency zirconia (LavaTM Plus), resin nanoceramic (LavaTM Ultimate), and hybrid ceramic (VITA ENAMIC®) CAD/CAM blocks (5 groups, n = 32). The specimen color and translucency parameter (TP) were assessed using a spectrophotometer at baseline and after subjecting the specimens to different staining solutions (coffee, cola, ginger, and water). Changes in color (ΔE) and TP (ΔTP) were calculated. The data was analyzed using the analysis of variance (ANOVA) and Tukey's post hoc test (p < 0.05). The correlation between ΔE and ΔTP was investigated using Pearson's correlation coefficient. RESULTS: Staining significantly affected the baseline color of all specimens. Ginger had the most significant effect on Lava Plus (ΔE = 4.01 ±1.2), cola on Celtra Duo (ΔE = 2.29 ±0.25) and coffee on Lava Ultimate (ΔE = 2.59 ±0.17). Generally, IPS-e.max-CAD showed the smallest ΔE. No significant differences in ΔTP were found between different staining solutions. Increased ΔE correlated with decreased translucency for all the tested materials and staining solutions. CONCLUSIONS: Staining had a marked effect on the color and translucency of the tested CAD/CAM materials. The color change was staining solutionand material-dependent, with IPS-e.max-CAD showing the greatest color stability.
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How this classification was reachedexpand
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) | 0.002 | 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 teacher head, 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".