Evaluation of “All‐on‐Four” Concept and Alternative Designs with 3D Finite Element Analysis Method
Bibliographic record
Abstract
PURPOSE: The aim of the present study was to evaluate the effect of the forces on the implant and supporting alveolar ridge in "all on four" concept and alternative designs using finite element analysis. MATERIALS AND METHODS: Different treatment alternatives with various implant designs were performed in an edentulous mandible. In Design 1, implants were placed according to "All-on-Four" concept; Design 2, two long (13 mm long, 4 mm diameter) and two short (7 mm long, 4 mm diameter) implants; Design 3, four long and two short implants; and Design 4, two long and four short implants were placed vertically. A force of 100 N for each tooth, a total of 300 N load was applied. Finite element analysis was used to evaluate and compare the different designs. RESULTS: The stress concentration within the cortical bone was significantly higher than the trabecular bone around the neck of the implants. The maximum stress values were located around the cortical bone of the distal implant for all designs. The reduction in the number of implants did not diminish the success of the design. CONCLUSIONS: In the presence of vertically resorbed posterior mandibula, although the "all on four" concept is a feasible approach clinically, short implants had decreased the amount of force transmitted to the supporting bone.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".