Effect of Staining Drinks on the Color Stability of Grit Blasted and Non-Grit Blasted Monolithic Zirconia: An In Vitro Study
Bibliographic record
Abstract
The present study aimed to compare the color stability of different types of zirconia with and without grit blasting (GB) after they were immersed in staining drinks. Two hundred and forty zirconia samples (N = 240) belonging equally to three different types of zirconia (Cercon® xt multilayer, xt extra translucent, and ht high translucent) were used in this study. Forty samples from each zirconia group were roughened with GB, while the other forty remained non-GB (NGB). Eight GB and NGB zirconia samples from each sub-group were immersed in artificial saliva, coffee, protein shake, chlorhexidine mouthwash, and a soft drink. Besides chlorhexidine mouthwash (immersion time: 14 days), the samples were immersed in the liquids for 28 days. A spectrophotometer was utilized to observe the color differences (∆E) at baseline (T0), 7 days post-immersion (T1), 14 days post-immersion (T2), 21 days post-immersion (T3), and 28 days post-immersion (T4). For the multilayer zirconia, the greatest ∆E (8.45 for GB and 5.97 for NGB samples) was observed after immersion in coffee at T4. For the extra translucent zirconia, the greatest ∆E (9.10 for GB and 6.81 for NGB samples) was also observed after immersion into the coffee at T4. For the high translucent zirconia, the greatest ∆E (4.53 for GB and 3.62 for NGB samples) was observed after immersion into the coffee at T4 and T3. Protein shake and soft drink immersion also significantly discolored some zirconia samples. Overall, GB zirconia samples presented with greater ∆E values than their NGB counterparts. It can be concluded that coffee immersion of zirconia samples caused a more significant discoloration (increased ∆E values) than any other liquid. Future clinical studies should be carried out to corroborate the current study’s findings.
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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 | 0.001 |
| 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".