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 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 | 0.000 | 0.001 |
| 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".