Evaluation of Tension and Deformation in a Mandibular Toronto Bridge Anchored on Three Fixtures Using Different Framework Materials, Abutment Systems, and Loading Conditions: A FEM Analysis
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to investigate by finite element method analysis the behaviour of a three-implant mandible Toronto framework made by three different materials, with two abutment systems and two loading conditions. MATERIALS AND METHODS: Three implants were virtually inserted in a mandible model in positions 3.6, 4.1, and 4.6. Three prosthetic framework bars with the same design and dimension (4.8 × 5.5 mm) were projected. The variables introduced in the computer model were the framework materials (glass fiber reinforced resin, Co-Cr, TiAl6V4), the abutment systems (Multi-Unit-Abutment [MUA]/OT-Bridge), and the loading conditions (500 N vertical load on all the framework area and 400 N on a 7-mm distal cantilever). The computer was programmed with physical properties of the materials as derived from the literature. Maximum tension and deformation values for each variable were registered at framework, screws, and abutment level and then compared. RESULTS: Metal frameworks Cr-Co and TiAl6V4 resulted in lower deformation than glass fiber-reinforced resin frameworks while presenting higher tension values. The OT-Bridge exhibited lower maximum tension and deformation values than the MUA system. The first loading condition reached higher tension and deformation values than the second and it resulted in more uniformly distributed load on all the framework area, especially with the OT-Bridge system. CONCLUSION: More rigid materials and OT-Bridge system decrease the deformation on the prosthetic components. Tension stresses are more uniformly distributed with glass fiber-reinforced resin, in the OT-Bridge system and avoiding cantilever loading.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".