The possibility of clinical bonding between metal/ceramic brackets to zirconia: in vitro study
Bibliographic record
Abstract
Objective: The present study aimed to assess the bond strength and durability of six bonding agents concerning their application to metal or ceramic brackets and zirconia. Materials and Methods: Six resin cement bonding agents (XT, XTS, RSBU, RGBU, SBPM, and GMP) were chosen for this investigation. Specimens were either stored in distilled water at 37°C for 24 h or subjected to 5,000 thermocycles before conducting a Shear Bond Strength (SBS) test. Statistical analysis of the SBS data was performed using three-way ANOVA and Games-Howell tests (α = 0.05). The Adhesive Remnant Index was examined, and the debonding surface details on brackets and zirconia were observed. Results: For metal brackets, all groups demonstrated clinically acceptable bond strength, irrespective of storage conditions, except for the XT group. Regarding ceramic brackets, all groups displayed acceptable bond strength after 24 h of water storage. However, following thermocycling, a significant decrease in SBS was noted across all groups (p < 0.05), with SBPM exhibiting a higher bond strength. Three-way ANOVA analysis indicated that SBS values were notably influenced by each factor, and an interaction among the three independent variables was observed (p = 0.000). Conclusion: The reliable bond strength between ceramic brackets and zirconia was significantly lower after thermocycling compared to that of metal brackets and zirconia. SBPM exhibited consistent and robust bond strength between ceramic/metal brackets and zirconia across various storage conditions. Furthermore, the HEMA-free adhesive demonstrated a potentially more consistent bonding performance compared to the HEMA-containing adhesive employed in this study.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".