Influence of Abutment Design on Stiffness, Strength, and Failure of Implant‐Supported Monolithic Resin Nano Ceramic (<scp>RNC</scp>) Crowns
Bibliographic record
Abstract
BACKGROUND: Recent technical development allows the digital manufacturing of monolithic reconstructions with high-performance materials. For implant-supported crowns, the fixation requires an abutment design onto which the reconstruction can be bonded. PURPOSE: The aim of this laboratory investigation was to analyze stiffness, strength, and failure modes of implant-supported, computer-assisted design and computer-aided manufacturing (CAD/CAM)-generated resin nano ceramic (RNC) crowns bonded to three different titanium abutments. MATERIALS AND METHODS: Eighteen monolithic RNC crowns were produced and loaded in a universal testing machine under quasi-static condition according to DIN ISO 14801. With regard to the type of titanium abutment, three groups were defined: (1) prefabricated cementable standard; (2) CAD/CAM-constructed individualized; and (3) novel prefabricated bonding base. Stiffness and strength were measured and analyzed statistically with Wilcoxon rank sum test. Sections of the specimens were examined microscopically. RESULTS: Stiffness demonstrated high stability for all specimens loaded in the physiological loading range with means and standard deviations of 1,579 ± 120 N/mm (group A), 1,733 ± 89 N/mm (group B), and 1,704 ± 162 N/mm (group C). Mean strength of the novel prefabricated bonding base (group C) was 17% lower than of the two other groups. Plastic deformations were detectable for all implant-abutment crown connections. CONCLUSIONS: Monolithic implant crowns made of RNC seem to represent a feasible and stable prosthetic construction under laboratory testing conditions with strength higher than the average occlusal force, independent of the different abutment designs used in this investigation.
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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) | 0.000 | 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".