Abutment Design for Implant‐Supported Indirect Composite Molar Crowns: Reliability and Fractography
Bibliographic record
Abstract
PURPOSE: To investigate the reliability of titanium abutments veneered with indirect composites for implant-supported crowns and the possibility to trace back the fracture origin by qualitative fractographic analysis. MATERIALS AND METHODS: Large base (LB) (6.4-mm diameter base, with a 4-mm high cone in the center for composite retention), small base (SB-4) (5.2-mm base, 4-mm high cone), and small base with cone shortened to 2 mm (SB-2) Ti abutments were used. Each abutment received incremental layers of indirect resin composite until completing the anatomy of a maxillary molar crown. Step-stress accelerated-life fatigue testing (n = 18 each) was performed in water. Weibull curves with use stress of 200 N for 50,000 and 100,000 cycles were calculated. Probability Weibull plots examined the differences between groups. Specimens were inspected in light-polarized and scanning electron microscopes for fractographic analysis. RESULTS: Use level probability Weibull plots showed Beta values of 0.27 for LB, 0.32 for SB-4, and 0.26 for SB-2, indicating that failures were not influenced by fatigue and damage accumulation. The data replotted as Weibull distribution showed no significant difference in the characteristic strengths between LB (794 N) and SB-4 abutments (836 N), which were both significantly higher than SB-2 (601 N). Failure mode was cohesive within the composite for all groups. Fractographic markings showed that failures initiated at the indentation area and propagated toward the margins of cohesively failed composite. CONCLUSIONS: Reliability was not influenced by abutment design. Qualitative fractographic analysis of the failed indirect composite was feasible.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".