Effect of In-Office Bleaching on Color and Surface Roughness of Composite Restoratives
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to determine color changes and surface roughness of composites when they were subjected to in-office bleaching. METHODS: 12 discs 15 mm in diameter and 2 mm thick were prepared from two shades (A2 & A4) of two composites, Durafil VS (DF) and TPH3 (TPH). Specimens were polished and stored in distilled water for 24 hours at 37 degrees C before being subjected to bleaching, staining, and re-bleaching. Each of the groups of specimens (DF-A2, DF-A4, TPH-A2 and TPH-A4) were subdivided into three subgroups (n=4) and bleached with Beyond, LumaWhite-Plus, and Opalescence-Boost. Specimens were then stained by immersing them in a coffee solution for 48 hours at 37 degrees C, and then they were re-bleached. Colorimetric measurements were performed at baseline, after bleaching, after staining, and after re-bleaching. Surface roughness was determined with environmental SEM before and after bleaching. Data were statistically-analyzed. RESULTS: None of the bleaching systems notably changed the color of composites (delta-E<2). Coffee staining affected DF specimens more than TPH. Stained specimens showed variable responses to whitening with no significant color change observed with TPH (delta-E<2) and significant changes observed with DF. Surface roughness significantly changed with bleaching, but the degree varied according to composite shade and bleaching agent. CONCLUSIONS: Three in-office bleaching agents had no significant color changes on two composites. DF showed more color change than TPH when immersed in coffee. Stained composites showed different degrees of whitening, with DF showing more response. Bleaching may adversely affect the surface texture of composites. Dentists should take into consideration that composite restorations may not respond to bleaching in the same way that natural teeth do.
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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 | 0.001 | 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 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".