Impact of Bone Augmentation of Facial Bone Defect around Osseointegrated Implant: A Three Dimensional Finite Element Analysis
Bibliographic record
Abstract
(1) Background: When dental implants are placed at the esthetic zone, facial bone fenestration might be expected. This study aimed to evaluate the biomechanical effect of bone augmentation around implants with facial bone fenestration defects using the finite element method. (2) Methods: An anterior maxillary region model with facial concavity was constructed with a threaded implant inserted following the root direction, resulting in apical threads exposure to represent the fenestration model. Several bone coverage levels were simulated by gradually shifting the deepest concavity point buccally, mimicking bone augmentation surgeries with different bone fill results. Oblique forces were applied, and analysis was performed. (3) Results: Peak compressive stress magnitude and distribution varied according to the level of exposure and facial concavity depth. The fenestration model demonstrated a slightly lower peak peri-implant bone stress, smaller implant displacement, and smaller bone volume with strain levels above 200 µ strain. A gradual increase in compressive stress, implant displacement, and bone volume exhibited strain level above 200 µ strain was observed with the increased bone fill level of the facial bone fenestration. (4) Conclusions: Exposure of implants apical threads at the maxillary anterior region does not significantly affect the peri-implant stress and strain results. However, increasing the buccolingual width and eliminating the buccal concavity might increase the peri-implant bone volume exhibited favorable loading levels.
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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 | 0.001 | 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".