Favorable residual stress induction by resin-cementation on dental porcelain
Bibliographic record
Abstract
Despite developments in polycrystalline ceramics, glassy dental-ceramic materials provide the optimum cosmetic option in most clinical situations to mimic the natural dentition. The clinical success of glassy dental-ceramic materials is often attributed to resin-adhesive bonding techniques. In this study we explore whether shrinkage stresses generated on photo-polymerisation of the resin-cement are sufficient to induce ceramic surface defect stabilization, and we quantify the transient nature of the induced stresses. Stress-induced changes in a feldspathic ceramic over a range of thicknesses (0.5–2.0 mm: n = 20 per thickness) were measured using a profilometric technique at baseline for each disc-shaped specimen (mean of the maximum deflection (δbaseline)) and again following polymerisation of a controlled resin-cement thickness on the contra-lateral surface. Measurements were repeated at 30, 60, 90 and 1440 min following photo-polymerization (δ30, δ60, δ90 and δ1440, respectively) before bi-axial flexure strength (BFS) determination at 24 h. A repeated measures ANOVA and post-hoc Bonferroni tests determined that δ1440 was significantly different from δbaseline (p = 0.02), δ30 (p < 0.01) and δ60 (p < 0.01) but not δ90 (p = 0.61). Data exploration revealed that there were differences in directionality of the independent variable (mean of the maximum deflection (δ)) with a proportion of specimens increasing in deflection and others reducing. The directionality of the effect strongly correlated with the BFS values. Where δ1440-δbaseline was low and/or negative, BFS values were also low. High BFS values were observed when δ1440-δbaseline was large and positive (indirectly inferring high shrinkage-stress-induced-deformation). A link between shrinkage stresses associated with the photo-polymerization of dental resin-based cements and the reinforcement of dental porcelain has clearly been established.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".