Tilted and Short Implants Supporting Fixed Prosthesis in an Atrophic Maxilla: A <scp>3D‐FEA</scp> Biomechanical Evaluation
Bibliographic record
Abstract
PURPOSE: This study compared the biomechanical behavior of tilted long implant and vertical short implants to support fixed prosthesis in an atrophic maxilla. MATERIALS AND METHODS: The maxilla model was built based on a tomographic image of the patient. Implant models were based on micro-computer tomography imaging of implants. The different configurations considered were M4S, four vertical anterior implants; M4T, two mesial vertical implants and two distal tilted (45°) implants in the anterior region of the maxilla; and M6S, four vertical anterior implants and two vertical posterior implants. Numerical simulation was carried out under bilateral 150 N loads applied in the cantilever region in axial (L1) and oblique (45°) (L2) direction. Bone was analyzed using the maximum and minimum principal stress (σmax and σmin ), and von Mises stress (σvM ) assessments. Implants were analyzed using the σvM . RESULTS: The higher σmax was observed at: M4T, followed by M6S/L1, M6S/L2, M4S/L2, and M4S/L1 and the higher σvM : M4T/L1, M4T/L2 and M4S/L2, M6S/L2, M4S/L1, and M6S/L1. CONCLUSIONS: The presence of distal tilted (all-on-four) and distal short implants (all-on-six) resulted in higher stresses in both situations in the maxillary bone in comparison to the presence of vertical implants (all-on-four).
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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) | 0.000 | 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) | 0.003 | 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".