Influence of Polishing Systems on Surface Roughness of Composite Resins: Polishability of Composite Resins
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Bibliographic record
Abstract
OBJECTIVES: study was to compare, with a threshold value of 200 nm, the surface roughness obtained when using 12 different polishing systems on four different composite resins (microfill, nanofill, and two nanohybrids). METHODS AND MATERIALS: A total of 384 convex specimens were made using Durafill VS, Filtek Supreme Ultra, Grandio SO, and Venus Pearl. After sandblasting and finishing with a medium-grit finishing disc, initial surface roughness was measured using a surface roughness tester. Specimens were polished using 12 different polishing systems: Astropol, HiLuster Plus, D♦Fine, Diacomp, ET Illustra, Sof-Lex Wheels, Sof-Lex XT discs, Super-Snap, Enhance/Pogo, Optrapol, OneGloss and ComposiPro Brush (n=8). The final surface roughness was measured, and data were analyzed using two-way analysis of variance. Pairwise comparisons were made using protected Fisher least significant difference. RESULTS: <0.05). The highest surface roughness was observed for all composite resins polished with OneGloss and ComposiPro Brush. Enhance/Pogo and Sof-Lex Wheels produced a mean surface roughness greater than the 200-nm threshold on Filtek Supreme Ultra, Grandio SO, and Venus Pearl. Data showed that there was an interaction between the composite resins and the polishing systems. CONCLUSIONS: A single polishing system does not perform equally with all composite resins. Except for Optrapol, multi-step polishing systems performed generally better than one-step systems. Excluding Enhance/Pogo, diamond-impregnated polishers led to lower surface roughness. Durafill VS, a microfill composite resin, may be polished more predictably with different polishers.
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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) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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 it