At‐Home and In‐Office Bleaching Protocols on the Color Match of Restorations Made With Single‐Shade Composites
Bibliographic record
Abstract
ABSTRACT Objective This study evaluated the color match of restorations made with single‐shade composites following two protocols of tooth bleaching. Materials and Methods Cavities in the cervical third of bovine crowns were restored using single‐shade composites (Omnichroma or Vittra Unique) or a multi‐shade composite restoration strategy (Filtek Z350 XT). The color of both the restoration and the middle third of the unrestored tooth were recorded. The Whiteness Index for Dentistry (WID) and color differences (Δ E 00 ) between the two areas were calculated. Specimens underwent bleaching using in‐office or at‐home protocols, and color evaluations were repeated at 24 h, 7 days, and 14 days post‐bleaching. Data were analyzed using MANOVA and repeated measures ANOVA. Results Bleaching protocols did not significantly affect WID or Δ E 00 values. The highest WID values were observed for Vittra Unique, and the lowest for Filtek Z350 XT. Tooth bleaching did not affect Δ E 00 values for restorations made with the multi‐shade strategy, but it resulted in a reduction for those made with the single‐shade strategy, regardless of the composite brand. Conclusion Tooth bleaching protocols differentially affected the color match of restorations made using single‐shade composites compared to multi‐shade restorations. Clinical Relevance Tooth bleaching protocols have a limited effect on the color of composites but can influence the color match between the restoration and the surrounding enamel. Specifically, color mismatches tend to decrease for lighter materials, such as single‐shade composites, after tooth bleaching, improving the overall esthetic integration of the restoration with the natural tooth structure.
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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 | 0.002 |
| 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".