Effect of Dentin-cleaning Techniques on the Shear Bond Strength of Self-adhesive Resin Luting Cement to Dentin
Bibliographic record
Abstract
OBJECTIVE: This in vitro study evaluated the influence of different cleansing techniques on the bond strength of self-adhesive cement to dentin. METHODS AND MATERIALS: A total of 33 noncarious human molars were sectioned mesiodistally and embedded in chemically cured resin with the buccal or lingual surfaces facing upward. Superficial dentin was exposed and resin disk provisional restorations were cemented to the dentin surfaces with noneugenol provisional cement and were stored in distilled water at 37°C. After seven days, the provisional restorations were removed and 13 specimens were randomly assigned to each of the five groups (n=13), according to the following cleansing treatments: G1-excavator (control); G2-0.12% chlorhexidine digluconate; G3-40% polyacrylic acid; G4-mixture of flour pumice and water; and G5-sandblasting with 50 μm aluminum oxide particles at a pressure of 87 psi. Resin composite disks (Filtek Supreme Plus, 3M ESPE Dental Products, St Paul, MN, USA) 4.7 (±0.1) mm in diameter and 3.0 (±0.5) mm in height were cemented with self-adhesive cement (RelyX Unicem, 3M ESPE), photocured, and stored in distilled water at 37°C for 24 hours. Shear bond strength testing was conducted using a universal test machine at a crosshead speed of 0.5 mm/min until failure. RESULTS: Data were analyzed using analysis of variance (ANOVA) and the Tukey-B rank order test. Sandblasting with aluminum oxide (11.32 ± 1.70 MPa) produced significantly higher shear bond strength values compared with any other treatment groups (p<0.05). No significant differences were found between G1-control (7.74 ± 1.72 MPa), G2-chlorhexidine (6.37 ± 1.47 MPa), and G4-pumice (7.33 ± 2.85 MPa) (p<0.05).
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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) | 0.000 | 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".