Effect of silorane-based adhesive system on bond strength between composite and dentin substrate
Bibliographic record
Abstract
CONTEXT: The complexities of the oral environment, the dentin substrate, and the different bond and composite resin systems represent a challenge to the maintenance of reasonable bond between the composite resin and the tooth structure. AIMS: To evaluate the effect of the adhesive system on bond strength between silorane-based composite resin and dentin. MATERIALS AND METHODS: Fourteen human molars extracted were selected and vertically cut into 3 dentin fragments, randomly divided among the experimental groups and restored with Z250 and P90 composite resin using different adhesive protocols (Adper Single Bond 2, Silorano primer, Adper SE Plus, and Scotchbond Multiuse). Two composite resin cylinders were built up on each dentin surface (n = 10) and subjected to a micro-shear bond strength test. STATISTICAL ANALYSIS USED: Kruskal-Wallis one-way analysis of variance and Tukey test (P = 0.05). RESULTS: According to the results, Kruskal-Wallis test evidenced at least one statistical significant difference (P = 0.001). The Tukey test showed statistically significant differences among the group (P < 0.05). Group PSM8 (P90 + SM) showed statically significant higher results when compared with groups PSP4 (P90 + SP), PSB2 (P90 + SB), and ZSE5 (Z250 + SE). CONCLUSION: The results evidenced that the monomer of the adhesive system has an effect on bond strength between the composite resin and dentin.
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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) | 0.002 | 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".