The Implant‐Abutment Interface of Alumina and Zirconia Abutments
Bibliographic record
Abstract
BACKGROUND: Although ceramic and titanium abutments are widely used in clinical practice, the mechanical characterization of the implant-abutment interface for ceramic abutments has not been evaluated after the dynamic loading. PURPOSE: The purpose of this study was to assess the implant-abutment interface after the dynamic loading of titanium, alumina, and zirconia abutments. MATERIALS AND METHODS: Fifteen aluminum oxide, zirconium oxide, and titanium abutments were manufactured by the Procera System (Nobel Biocare AB, Göteborg, Sweden) and were connected to Ø 3.75 x 13-mm regular platform implants (MK III, Nobel Biocare AB) secured in a 30 degrees inclined plane. A mechanical testing machine applied compressive dynamic loading between 20 and 200 N at 1 Hz on a standard contact area of copings cemented on abutments for 47.250 cycles. The measurements of microgaps at the implant-abutment interface from the labial, palatinal, mesial, and distal surfaces of each specimen were undertaken by scanning electron microscope analyses prior to and after the experiments. The data of the microgaps before and after the dynamic loading were statistically assessed using the Wilcoxon signed rank test and the Kruskal-Wallis variance analysis (alpha = 0.05). RESULTS: Coping fracture, abutment fracture, or abutment screw loosening or fracture was not detected in any specimen during the entire test period. After the dynamic loading, the titanium abutment control group revealed an increased microgap (3.47 microm) than zirconia (1.45 microm) and alumina (1.82 microm) groups at the palatinal site (p < .05). The mean measurement values at different measurement sites of specimens within and between each abutment group were similar (p > .05). CONCLUSION: Owing to their comparable microgap values at the implant-abutment interface after the dynamic loading, ceramic abutments can withstand functional forces like conventional titanium abutments.
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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 | 0.001 |
| 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".