Influence of Preparation and Wall Thickness on the Resistance to Fracture of Zirconia Implant Abutments
Bibliographic record
Abstract
BACKGROUND: Studies about the effect of grinding procedures as well as material thickness on the resistance of zirconia implant abutments are in short supply. PURPOSE: This study evaluated the effect of wall thickness as well as preparation on the resistance of zirconia implant abutments. MATERIALS AND METHOD: Sixty-four implants received titanium (group Ti) and zirconia abutments (groups Zr-8, Zr-18, and Zr-1). The abutments of group Zr-8 had a 0.8-mm wall thickness, whereas the wall thickness of group Zr-18 was reduced by preparation from 1 mm to 0.8 mm. The abutments of group Zr-1 had a wall thickness of 1 mm. Standardized maxillary central incisor metal crowns were cemented on all abutments. All specimens were then tested in a universal testing machine for their resistance to fracture before and after masticatory simulation (n = 8). RESULTS: The median resistance to fracture values (N) before and after aging were, respectively: group Ti: 500-504; group Zr-8: 487-491; group Zr-18: 490-451; and group Zr-1: 519-480. No significant effects of group, aging, or combinations were found (p > .05). CONCLUSION: All tested abutments have the potential to withstand physiologic occlusal forces in the anterior region (> 200 N). The applicability of the results to other implant systems should be verified.
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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 | 0.002 |
| 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) | 0.001 | 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".