Effects of sterilization, conditioning, and thermal aging on the retention of zirconia hybrid abutments: A laboratory study
Bibliographic record
Abstract
OBJECTIVE: To investigate the effects of sterilization, conditioning method, and thermal aging on the retentive strength of two-piece zirconia abutments. MATERIALS AND METHODS: In total, 128 stock zirconia abutments were divided into four groups (n = 32) according to the conditioning parameters: (A) air-abrasion using 50 μm alumina particles/1.0 bar, (B) 50 μm/2.0 bar, (C) 100 μm/1.0 bar, and (D) 100 μm/2.0 bar. All abutments were bonded onto titanium bases using DTK adhesive resin and stored in water bath (37°C) for 72 h. Each group was subdivided into two subgroups (n = 16), group 1 was disinfected, whereas group 2 followed disinfection and autoclave sterilization. Half of the specimens of each subgroup (n = 8) was directly subjected to the axial retention test (groups N), while the other half was first subjected to 150 days of thermocycling followed by retention test (groups T). Statistical analysis was performed with three-way ANOVA, additional statistical analysis was performed by using separate one-way ANOVAs followed by the Tukey's post-hoc test for post hoc pairwise comparisons among groups. RESULTS: The highest median retention strength was recorded for group B2N (1390 N), whereas the lowest strength was recorded for group C1T (688 N). No significant interaction (p ≥ 0.05) was detected between the different variables; conditioning method, sterilization, and the thermal cycling regarding the effect on the resulting retention. However, the sterilization always showed a positive effect. Thermocycling presented an adversely significant effect only in the absence of sterilization (p < 0.05), with the exception of subgroups A. For the sterilized groups, thermocycling had no statistically significant effect on the retention. CONCLUSION: Steam autoclaving increased the retention of hybrid zirconia abutments. DTK adhesive resin for two-piece zirconia abutments performed well after sterilization and thermocycling.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".