Morphologic Texture Characterization Allied to Cigarette Smoke Increase Pigmentation in Composite Resin Restorations
Bibliographic record
Abstract
STATEMENT OF THE PROBLEM: The staining effect of tobacco smoke on resin color is clinically observed. However, there is no evidence determining whether this staining is increased on texturized surfaces or if the color change is superficial and can be removed by repolishing procedures. OBJECTIVE: To evaluate the effect of tobacco smoke on the color of a composite with smooth or texturized surfaces, before and after repolishing. MATERIALS AND METHODS: Forty composite specimens were allocated into four groups (N = 10): Smooth surface not exposed (G1) and exposed to tobacco smoke (G2), texturized surface not exposed (G3), and exposed to tobacco smoke (G4). During 21 days, G2 and G4 were daily exposed to the smoke from 20 cigarettes. Color measurements were carried out at baseline, after 21 days, and after repolishing. Variables L* (luminosity), b* (blue-yellow), and DeltaE (total color change) were statistically analyzed (repeated measures analysis of variance/Tukey). RESULTS: Texturized and smooth specimens presented similar luminosity at baseline. Tobacco smoke significantly reduced L* in G2 and G4, and increased b* in G4. After repolishing, L* increased and b* reduced in stained groups, but values continued to be different from baseline ones. Repolishing significantly reduced DeltaE, but values from stained groups were still greater than that from unstained groups. CONCLUSION: Tobacco smoke changes the color of composite, and surface texture can increase the staining. Repolishing reduces superficial staining, but this procedure may not return the composite to baseline color. CLINICAL RELEVANCE: Smokers that will receive anterior composite restorations should be warned about the negative cosmetic effect of the smoking habit on the color of restorations.
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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) | 0.000 | 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".