In Vitro Evaluation of Surface Properties and Wear Resistance of Conventional and Bulk-fill Resin-based Composites After Brushing With a Dentifrice
Bibliographic record
Abstract
OBJECTIVES: This study evaluated the effect of toothbrushing with a dentifrice on gloss, roughness profile, surface roughness, and wear of conventional and bulk-fill resin-based composites. METHODS AND MATERIALS: Gloss and surface roughness of resin-based composites (RBCs; Admira Fusion X-tra, Aura Bulk Fill, Filtek Bulk Fill Flowable, Filtek Bulk Fill Posterior Restorative, Filtek Supreme Ultra, Herculite Ultra, Mosaic Enamel, SDR flow+, Sonic Fill 2, Tetric EvoFlow Bulk Fill and Tetric EvoCeram Bulk Fill) were analyzed before and after brushing; the roughness profile and wear were also determined after toothbrushing. Representative three-dimensional images of the surface loss and images comparing the unbrushed and brushed surfaces were also compared. Analysis of variance and Tukey post hoc tests were applied (α=0.05) to the gloss, surface roughness, roughness profile, and surface loss data. Pearson's correlation test was used to determine the correlation between gloss and surface roughness, surface loss and percentage of gloss decrease after brushing, and surface loss and surface roughness after brushing. RESULTS: <0.05). A significant negative correlation was found between gloss and surface roughness, and a weak correlation was found between the decrease in gloss and the extent of surface loss, and any increase in surface roughness and the surface loss. CONCLUSIONS: Toothbrushing with a dentifrice reduced the gloss, increased the surface roughness, and caused loss at the surface of all the RBCs tested. Considering all the properties tested, Mosaic Enamel exhibited excellent gloss retention and a low roughness profile and wear, while Admira Fusion X-tra exhibited the greatest decrease in gloss, the highest roughness profile, and the most wear.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".