Comparison of Fracture Resistance and Fit Accuracy of Customized Zirconia Abutments with Prefabricated Zirconia Abutments in Internal Hexagonal Implants
Bibliographic record
Abstract
BACKGROUND: Customized zirconia abutments are increasingly applied for the fabrication of esthetic implant restorations aimed at imitating the natural situation. These abutments are individually shaped according to the anatomical needs of the respective implant site. PURPOSE: This study sought to compare the fracture resistance and fit accuracy of prefabricated and customized zirconia abutments using an internal hexagonal implant system (TSV®, Zimmer, Carlsbad, CA, USA). MATERIALS AND METHODS: Two zirconia abutment groups were tested: prefabricated zirconia abutments (ZirAce, Acucera, Seoul, Korea) and customized zirconia abutments milled by the Zirkonzahn milling system. Twenty zirconia abutments per group were connected to implants on an acrylic resin base with 30-Ncm torque. The fracture resistance of zirconia abutments was measured with an angle of 30° at a crosshead speed of 1 mm/min using the universal testing machine (Z020, Zwick, Ulm, Germany). Marginal and internal gaps between implants and zirconia abutments were measured after sectioning the embedded specimens using a digital microhardness tester (MXT70, Matsuzawa, Tokyo, Japan). RESULTS: The customized abutments were significantly stronger (1,430.2 N) than the prefabricated abutments (1,064.1 N). The mean marginal adaptation of customized abutments revealed a microgap that was increased (11.5 µm) over that in prefabricated abutments (4.3 µm). CONCLUSION: Within the limitations of this study, the customized abutments are significantly stronger than prefabricated abutments, but the fit is less accurate. The strength and fit of both abutments are within clinically acceptable limit.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".