Influence of Implant Geometry on the Surface Strain Behavior of Peri‐Implant Bone: A <scp>3D</scp> Analysis
Bibliographic record
Abstract
To ensure long-term implant success, it is crucial to understand the force transmission from the implant to the surrounding bone. In dentistry, bioengineering methods are applied to investigate these processes. The aim of this study was to analyze the influence of different implant geometries on the surface strain behavior of porcine mandibles under load using a 3D optical camera system in combination with digital image correlation. Four different implant types were subjected to a force of 200 N in three different loading directions (axial, non-axial 15°, and non-axial 30°). Under axial loading, parallel-walled implants exhibited lower surface strain values on the peri-implant bone compared with tapered implants. However, when subjected to non-axial loading, these parallel-walled implants showed a substantial relative increase in strain by approximately a factor of 2.96 compared with axial conditions. At a 30° non-axial angle, long, tapered implants with a smaller diameter (BLX 3.75) produced lower peri-implant bone strains than implants with larger diameters and shorter lengths, while short, tapered implants (BLT) demonstrated a lower relative increase in strain (factor ~1.49) from axial to non-axial loading. Under non-axial loading, long, tapered implants with a small diameter resulted in lower strains in the peri-implant bone compared with implants with a larger diameter and shorter length. It was found that non-axial loads lead to higher strains than axial loads. Therefore, the success of implantation could be significantly influenced by selecting an appropriate implant geometry and the correct angulation of the implant.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".