Surface roughness of restorative materials after immersion in mouthwashes
Bibliographic record
Abstract
Abstract Objective: To evaluate the surface roughness of resin composite and ceramic material after immersion in mouthwashes. Methodology: Thirty specimens of resin composite and ceramic material were prepared with a stainless steel matrix (6 mm × 2 mm). The samples of each material were divided into three groups (n = 10), according to the mouthwashes: Distilled water (DW), chlorhexidine (CL) 0.12%, and cetylpyridinium chloride (CC). Specimens were individually submitted to the immersion cycle in 15 mL of mouthwash for 30 days, three times per day, for 1 min/cycle. Surface roughness measurements were performed at three different time intervals: Before the first cycle (T0), after 7 (T1), and 30 days (T2) of immersion. Data were analyzed by the two-way ANOVA and Tukey tests (P ≤ 0.05). Results: There was no statistically significant difference in surface roughness of resin composite among mouthwashes (DW - 1.4 ± 0.13 μm; CL - 1.16 ± 0.13 μm; CC - 1.18 ± 0.13 μm). Surface roughness was statistically significantly lower after 30 days (T2-0.56 ± 0.60 μm) compared with the initial period (T0-1.63 ± 0.60 μm) and after 7 days (T1-1.57 ± 0.60 μm). For ceramic material, CC (3.75 ± 0.60 μm) caused a higher level of surface roughness compared with DW (2.57 ± 0.60 μm) and CL (3.39 ± 0.60 μm). There was no statistically significant difference among the different time intervals (T0-3.05 ± 0.18 μm; T1-3.41 ± 0.18 μm; T2-3.26 ± 0.18 μm). Conclusion: Mouthwashes did not promote a significant change in surface roughness of composite resin. Cetylpyridinium chloride promoted an increase in surface roughness of dental ceramic.
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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 | 0.002 | 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) | 0.001 | 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 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".