Influence of curing protocol and ceramic composition on the degree of conversion of resin cement
Bibliographic record
Abstract
OBJECTIVE: Due to increasing of aesthetic demand, ceramic crowns are widely used in different situations. However, to obtain long-term prognosis of restorations, a good conversion of resin cement is necessary. To evaluate the degree of conversion (DC) of one light-cure and two dual-cure resin cements under a simulated clinical cementation of ceramic crowns. MATERIAL AND METHODS: Prepared teeth were randomly split according to the ceramic's material, resin cement and curing protocol. The crowns were cemented as per manufacturer's directions and photoactivated either from occlusal suface only for 60 s; or from the buccal, occlusal and lingual surfaces, with an exposure time of 20 s on each aspect. After cementation, the specimens were stored in deionized water at 37°C for 7 days. Specimens were transversally sectioned from occlusal to cervical surfaces and the DC was determined along the cement line with three measurements taken and averaged from the buccal, lingual and approximal aspects using micro-Raman spectroscopy (Alpha 300R/WITec®). Data were analyzed by 3-way ANOVA and Tukey test at =5%. RESULTS: Statistical analysis showed significant differences among cements, curing protocols and ceramic type (p<0.001). The curing protocol 3x20 resulted in higher DC for all tested conditions; lower DC was observed for Zr ceramic crowns; Duolink resin cement culminated in higher DC regardless ceramic composition and curing protocol. CONCLUSION: The DC of resin cement layers was dependent on the curing protocol and type of ceramic.
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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) | 0.003 | 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".