Do different adhesives influence the color stability and fluorescence of composite restorations after aging?
Bibliographic record
Abstract
This study aimed to evaluate the influence of dental adhesive color on the chromatic stability and fluorescence intensity of composite resin restorations of different thicknesses. Ninety bovine enamel samples were obtained and restored with resin composite varying thicknesses of restorative material and enamel (1.0 mm enamel and 1.0 mm composite; 1.5 mm enamel and 0.5 mm composite; 0.5 mm enamel and 1.5 mm composite). The restorations were made of composite resin (Opallis E-bleach H) using different types of dental adhesives: Ambar, Ambar APS, and Single Bond Universal (n=10). The samples were subjected to color measurement tests in a spectrophotometer using CIEDE2000 and fluorescence intensity measurements before and after aging in a red wine coloring solution. Data were subjected to analysis of variance (ANOVA) and Tukey's test (α = 0.05). There were no statistically significant changes in color stability or fluorescence intensity for restorations made of different materials or thicknesses (p>0.05). Single Bond Universal showed greater color stability at 0.5 mm thickness (ΔE00 = 4.4 ± 1.6) compared to other thicknesses of the same material (p=0.003), as well as a greater difference in fluorescence intensity after aging at 1.5 mm thickness (-414.9 ± 103.8) compared to other materials (p=0.0002). Overall, it was concluded that the different adhesive systems did not influence the color stability and fluorescence of restorations of different thicknesses.
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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".