Effect of surface treatment on the retention of zirconia crowns to tooth structure after aging
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of different surface treatments on the retention between zirconia crowns and tooth structure after in vitro aging. MATERIALS AND METHODS: Human third molars (n = 44) received crown preparations and CAD/CAM zirconia crowns were manufactured. Specimens were divided into two groups: no aging, control (NAC, n = 11) or aging (A, n = 33). NAC were bonded with universal adhesive (UA). Aged specimens were divided into three subgroups (n = 11) according to surface treatment: Control: no abrasion + UA; Alumina: alumina abrasion + UA; Silica: tribochemical silica coating + UA. The crowns were cemented with dual-cure resin cement. Specimens in group A were aged by a combination of mechanical, thermal, and pH cycling. Retention strength values were obtained by tensile tests and results were analyzed by one-way analysis of variance (ANOVA) and Tukey test (P < .05). RESULTS: Aging decreased the retention strength in control specimens (P < .001). Surface treatment improved the retention strength of aged specimens (P < .001), with similar results between alumina and tribochemical silica coating. CONCLUSION: The chemical interaction between the universal bonding system and zirconia's surface was not sufficient to withstand artificial aging. Tribochemical silica coating did not promote additional retention in comparison to alumina blasting. CLINICAL SIGNIFICANCE: The long-term retention of translucent zirconia crowns to tooth structure using phosphate-based materials is improved by means of mechanical surface treatments such as alumina blasting and tribochemical silica coating.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".