Microtensile bond strength of restorative composite bonded with self-adhesive resin cements to enamel and dentin
Bibliographic record
Abstract
PURPOSE: To determine the microtensile bond strength (µTBS) of composite restorations when bonded with self-adhesive resin-cements. METHODS: Thirty caries-free extracted molars were sterilized, and divided into 5 equal groups according to adhesive used: SBMP (Scotch-Bond-Multipurpose, total-etch 3-step adhesive, 3M/ESPE), PAN (PanaviaF-2.0, resin-cement with self-etch primer, Kuraray), RXU (RelyX-Unicem, self-adhesive resin-cement, 3M/ESPE), BRZ (Breeze, self-adhesive resin-cement, Pentron) and MON (Monocem, self-adhesive resin-cement, Shofu). Each group was divided into 2 subgroups (dentin or enamel). Bonding agents, used according to manufacturers’ directions, or a thin layer of resin cement was applied onto teeth flat surfaces. Six mm-thick Filtek-Z250 (3M/ESPE) composite build up was made in three increments. Teeth were sectioned to obtain rectangular specimens which were subjected to tensile force until failure. Specimens were subjected to 1,000 thermo-cycles between 5oC-55°C. Means and standard deviation (SD) were calculated and statistically-analyzed with ANOVA and Tukey’s t-test. Specimens’ failure modes were reported. RESULTS: SBMP showed the highest µTBS results with enamel (24.6(6.1) MPa), PAN showed high µTBS with enamel (12.1(3.9)MPa) and dentin (11.6(4.7)MPa) compared to the other self-adhesive cements. Failure modes were adhesive and mixed for self-adhesive resin-cements. MON subgroups and BRZ enamel subgroup underwent premature failure. CONCLUSION: self-adhesive resin-cements showed low µTBS compared to SBMP.
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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) | 0.000 | 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".