Influence of Different Immersion Solutions and Polishing Protocols on the Roughness of Conventional and CAD/CAM Restorative Materials
Bibliographic record
Abstract
ABSTRACT Objective: To evaluate the effects of immersion solutions and polishing protocols on the surface roughness of different restorative materials. Material and Methods: Specimens from composite resin (CR) (Filtek Z350 XT) and CAD-CAM blocks of resin nanoceramic (NC) (Lava Ultimate Restorative), hybrid ceramic (HC) (Enamic), and zirconia-reinforced lithium silicate (ZL) (Celtra Duo) were assigned to two protocols: only polishing rubbers (PR) (Ceramisté rubbers®) or PR + paste (Porcelize®) (PR+P). Surface roughness was measured before (T0), after 30 days (T1), and 60 days (T2) of immersion in solutions of artificial saliva (SA), coffee (CF), and Coca-Cola® (CO). Roughness changes were compared using ANOVA and Tukey test (α=0.05). Results: Time (p≤0.003) and the interaction of time and immersion solution (p≤0.03) significantly affected all materials. The interaction of time, immersion solution, and polishing significantly affected ZL (p=0.003) and NC (p=0.013). The highest surface roughness values were observed with CF solution at T2. Conclusion: Different polishing protocols did not significantly affect the restorative materials tested. The CF solution affected the surface roughness of composite resin and feldspathic-composite hybrid ceramic after 60 days, regardless of the polishing protocol. The effects of immersion solutions and polishing protocols vary and depend on the properties of each restorative material.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".