A Finite Element Analysis of Stress Distribution in Bone Tissue Surrounding Uncoupled or Splinted Dental Implants
Bibliographic record
Abstract
BACKGROUND: Several studies on one-stage surgery in the treatment of the edentulous maxilla with implant-supported fixed prostheses have reported problems with removable provisional prostheses, which can load the implants in an uncontrollable manner during healing, and jeopardize healing. Immediate splinting of the implants with a fixed provisional prosthesis has been proposed to protect the bone-implant interface. PURPOSE: This study used the finite element method (FEM) to simulate stresses induced in bone tissue surrounding uncoupled and splinted implants in the maxilla because of bite force loading, and to determine whether the differences in these stress levels are related to differences in observed bone losses associated with the two healing methods. MATERIALS AND METHODS: Stress levels in the maxilla were studied using the FEM program TRINITAS (Institute of Technology, Linköping University, Linköping, Sweden) in which all phases - preprocessing/modeling, equation solving, and postprocessing/evaluation - were simulated. RESULTS: Stress levels in bone tissue surrounding splinted implants were markedly lower than stress levels surrounding uncoupled implants by a factor of nearly 9. CONCLUSION: From a mechanical viewpoint, FEM simulation supports the hypothesis that splinting reduces damage evolution in bone tissue, which agrees with clinical observations.
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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 | 0.000 |
| 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) | 0.002 | 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".