In Vitro Effects of Sof-Lex, Eve, and Astropol Polishing Systems on Composite Resin Surface Roughness After Aging
Bibliographic record
Abstract
Objectives: Surface roughness is one of the important properties of composite restorations. Different polishing systems are used to provide an appropriate composite restoration surface. The aim of this study was to evaluate the effects of Sof-Lex, Eve, and Astropol polishing systems on composite resin surface roughness after aging. Materials and Methods: In this in vitro study, 36 composite discs (8×2mm) were fabricated. The specimens were randomly divided into three groups (N=12) for polishing with (I) Sof-Lex (3M ESPE), (II) Eve (Ernst Vetter GmbH), and (III) Astropol (Ivoclar/Vivadent) polishing systems. The specimens were then subjected to thermocycling. Surface roughness of the specimens was measured before and after polishing, and after thermocycling by a contact profilometer. Repeated Measures ANOVA was used to analyze the data (α=0.05). Results: Although Astropol showed slightly higher surface roughness in comparison to Sof-Lex and Eve, the level of surface roughness before and after polishing and after aging was not significantly different among the three polishing systems (P=0.704). Conclusion: Within the limitations of this in vitro study, Sof-Lex, Eve, and Astropol showed similar acceptable results with regard to composite resin surface roughness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| 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".