DYNAMIC STRESS DISTRIBUTION IN A MODEL OF IMPLANTED MANDIBLE: NUMERICAL ANALYSIS OF VISCOELASTIC BONE
Bibliographic record
Abstract
To determine the success of dental implants, mechanical stress distribution in the implant-bone interface is considered to be a determinant. Many researchers have used finite element modeling of implant-bone through applying static loading on the implant; however, dynamic loading has not extensively been investigated specially considering viscoelastic behavior of the bone. The aim of this study is to analyze effects of viscoelasticity of bone and dynamic loading comparable to mastication conditions on stress distribution in an implanted mandible. A three-dimensional finite-element model of an implanted mandible in the first molar region was constructed from computerized tomography data. Effects of several parameters, such as material properties including viscoelastic behavior of the cortical and trabecular bones, load amplitude, duration and direction on the instantaneous and long-term von Mises stress distribution of an implanted mandible were evaluated. In all loading conditions, the maximum von Mises stress occurred in cortical bone surrounding the neck of implant. Stress distribution was not noticeably affected by viscoelastic behavior during the first loading cycles, however, after 100 s periodic loading, the differences between stress magnitudes (especially in the cortical bone) became noticeable. In addition, sensitivity analysis showed that both cortical and trabecular bones were more sensitive to axial load than buccalingual and mesiodistal forces. The results of this study contribute to analysis of parameters involved in success of dental implantation.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".