Enhancing Shear Bond Strength in Lithium Silicate Glass Ceramics: Surface Treatment Optimization for Reseating Protocols
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The rapid evolution of lithium silicate-based glass ceramics in the field of dental ceramics has led to the availability of different compositions in the market. This in vitro study was conducted to assess an effective protocol for recementing de-bonded lithium silicate-based glass ceramics by evaluating the shear bond strength of three reseating methods. The study included IPS e.max® CAD, Vita Suprinity®, Celtra Duo®, and n!ce as lithium-based glass ceramics. The samples underwent a series of preparation steps, including embedding in acrylic resin, hand polishing, etching with 5% hydrofluoric acid, and application of universal primer and adhesive as per manufacturer instructions. Subsequently, adhesive resin cement was applied to the ceramic tablets, and shear bond strength was assessed using a standardized method. The findings revealed that no single method demonstrated significantly superior results compared to the others. However, it was observed that etching with 5% hydrofluoric acid for 20 s yielded favorable outcomes in terms of time efficiency and standardized results. Additionally, it was noted that although sandblasting increased surface area, it did not enhance bond strength due to unfavorable surface disturbance. In conclusion, the study suggests that etching with 5% hydrofluoric acid for 20 s is a favorable protocol for reseating de-bonded lithium disilicate-based glass ceramics, offering both time efficiency and consistent results for clinicians.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it