Maxillary Sinus Augmentation without Grafting Material with Simultaneous Implant Installation: A Three‐Dimensional Finite Element Analysis
Bibliographic record
Abstract
PURPOSE: The study aims to provide a theoretical guidance of postmaxillary implant in the augmented sinus without grafting materials by establishing a three-dimensional model of this new implant restorative technique, evaluating failure risk of sinus augmentation without grafting materials of different alveolar ridge heights, and analyzing stress distribution of different healing stage. MATERIALS AND METHODS: Seventeen three-dimensional finite element models of a posterior maxillary region with sinus mucosa and different elevation heights were constructed according to anatomical data of sinus area, and the standard implant model based on Nobel Biocare implant system were created via computer-aided design software. All materials were assumed to be isotropic and linearly elastic. Axial force of 150 N was applied. The von Mises stress, stress distribution, and implant displacement were calculated with software. RESULTS: With the height of the alveolar ridge reducing, the maximum von Mises stress of tissues and the displacement of the implant are on the rise, especially when the height of the bone is less than 7 mm. When the height decreased to 4 mm, the data may be doubled. After the stiff callus stage, the stress and displacement were close to the control model. CONCLUSION: For maxillary sinus augmentation without grafting material implant technique, the stress of different tissues and the displacement of the implant were not increased much when the height of alveolar ridge is more than 7 mm. But if the alveolar bone height is less than 4 mm, this implant technique is not suggested. Immediately loading is not suggested and the loading opportunity should be after the stiff callus stage at least to improve the success rate.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".