Effect of tooth brushing on gloss retention and surface roughness of five bulk‐fill resin composites
Bibliographic record
Abstract
Abstract Objectives To determine the effects of tooth brushing on five bulk‐fill resin based composites (RBCs). Method Ten samples of Filtek Supreme Enamel (control), Filtek One Bulk Fill, Tetric EvoCeram Bulk Fill, SonicFill 2, SDR flow+, and Admira Fusion X‐tra were light cured for 20 seconds using the Valo Grand curing light. After 24 hours storage in air at 37°C, specimens were brushed in a random order using Colgate OpticWhite dentifrice and a soft toothbrush. Surface gloss was measured prior to brushing, after 5,000, 10,000 and 15,000 back and forth brushing cycles. Surface roughness was measured after 15,000 brushing cycles using atomic force microscopy (AFM) and selected scanning electron microscope (SEM) images were taken. The data was examined using ANOVA and pair‐wise comparisons using Scheffe's post‐hoc multiple comparison tests (α = 0.05). Results Surface gloss decreased and the surface roughness increased after brushing. Two‐way ANOVA showed that both the RBC and the number of brushing cycles had a significant negative effect on the gloss. One‐way ANOVA showed that the RBC had a significant effect on the roughness after 15,000 brushing cycles. For both gloss and roughness, brushing had the least effect on the nano‐filled control and nano‐filled bulk‐fill RBC, and the greatest negative effect on Admira Fusion X‐tra. The SEM images provided visual agreement. There was an excellent linear correlation (R2 = 0.98) between the logarithm of the gloss and roughness. Conclusion After brushing, the bulk‐fill RBCs were all rougher than the control nano‐filled RBC. The nano‐filled bulk‐fill RBC was the least affected by brushing. Clinical Significance Bulk‐fill RBCs lose their gloss faster and become rougher than the nanofilled conventional RBC, Filtek Supreme Ultra. The nanofilled bulk‐fill RBC was the least affected by tooth brushing.
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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".