Biomechanical behavior of titanium and zirconia frameworks for implant‐supported full‐arch fixed dental prosthesis
Bibliographic record
Abstract
BACKGROUND: The biomechanical behavior of implant-supported titanium and zirconia full-arch fixed dental prosthesis (FAFDP) frameworks require further investigation. PURPOSE: Strains transferred by implant-supported titanium (Ti) and zirconia (Zr) FAFDP frameworks were analyzed. MATERIALS AND METHODS: Maxillary 14-unit FAFDPs supported by 6 implants and 12-unit FAFDPs supported by 4 implants were tested. One-piece frameworks were fabricated by computer-aided design/computer-aided manufacturing. Four groups were divided (n = 3): G1, Ti-6 implants; G2, Zr-6 implants; G3, Ti-4 implants; G4, Zr-4 implants. A 250 N single-point load was applied on the second premolar. A three-dimensional digital image correlation system recorded framework and maxilla model surface deformation. RESULTS: The following strains (μS) averaged over the length of the second premolar were calculated: frameworks, G1 (321.82 ± 111.29), G2 (638.87 ± 108.64), G3 (377.77 ± 28.64), G4 (434.18 ± 132.21); model surface, G1 (473.99 ± 48.69), G2 (653.93 ± 45.26), G3 (1082.50 ± 71.14), G4 (1218.26 ± 230.37). Zirconia frameworks supported by 6 implants (G2) presented higher surface strains (P < .05). FAFDPs with titanium frameworks transferred significantly lower strains to the supporting maxilla when 6 implants were used (G1) (P < .05). Both framework materials transferred similar strains when supported by 4 implants (G3 and G4) (P > .05). CONCLUSIONS: Zirconia frameworks supported by 6 implants showed higher strains. FAFDPs supported by 6 implants transferred less strains to the supporting maxilla, irrespective of framework material.
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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 | 0.000 |
| 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".