Polishing mechanism of light-initiated dental composite: Geometric optics approach
Bibliographic record
Abstract
BACKGROUND/PURPOSE: For light-initiated dental hybrid composites, reinforcing particles are much stiffer than the matrix, which makes the surface rugged after inadequate polish and favors bacterial adhesion and biofilm redevelopment. The aim of the study was to investigate the polishing mechanism via the geometric optics approach. METHODS: We defined the polishing abilities of six instruments using the obtained gloss values through the geometric optics approach (micro-Tri-gloss with 20°, 60°, and 85° measurement angles). The surface texture was validated using a field emission scanning electron microscope (FE-SEM). Based on the gloss values, we sorted polishing tools into three abrasive levels, and proposed polishing sequences to test the hypothesis that similar abrasive levels would leave equivalent gloss levels on dental composites. RESULTS: The three proposed, tested polishing sequences included: S1, Sof-Lex XT coarse disc, Sof-Lex XT fine disc, and OccluBrush; S2, Sof-Lex XT coarse disc, Prisma Gloss polishing paste, and OccluBrush; and S3, Sof-Lex XT coarse disc, Enhance finishing cups, and OccluBrush. S1 demonstrated significantly higher surface gloss than the other procedures (p < 0.05). The surface textures (FE-SEM micrographs) correlated well with the obtained gloss values. CONCLUSION: Nominally similar abrasive abilities did not result in equivalent polish levels, indicating that the polishing tools must be evaluated and cannot be judged based on their compositions or abrasive sizes. The geometric optic approach is an efficient and nondestructive method to characterize the polished surface of dental composites.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".