Finite‐Element Analysis of Stress on Dental Implant Prosthesis
Bibliographic record
Abstract
BACKGROUND: Understanding how clinical variables affect stress distribution facilitates optimal prosthesis design and fabrication and may lead to a decrease in mechanical failures as well as improve implant longevity. PURPOSE: In this study, the many clinical variations present in implant-supported prosthesis were analyzed by 3-D finite element method. MATERIALS AND METHOD: A geometrical model representing the anterior segment of a human mandible treated with 5 implants supporting a framework was created to perform the tests. The variables introduced in the computer model were cantilever length, elastic modulus of cancellous bone, abutment length, implant length, and framework alloy (AgPd or CoCr). The computer was programmed with physical properties of the materials as derived from the literature, and a 100N vertical load was used to simulate the occlusal force. Images with the fringes of stress were obtained and the maximum stress at each site was plotted in graphs for comparison. RESULTS: Stresses clustered at the elements closest to the loading point. Stress increase was found to be proportional to the increase in cantilever length and inversely proportional to the increase in the elastic modulus of cancellous bone. Increasing the abutment length resulted in a decrease of stress on implants and framework. Stress decrease could not be demonstrated with implants longer than 13 mm. A stiffer framework may allow better stress distribution. CONCLUSION: The relative physical properties of the many materials involved in an implant-supported prosthesis system affect the way stresses are distributed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".