Micromotion and Stress Distribution of Immediate Loaded Implants: A Finite Element Analysis
Bibliographic record
Abstract
BACKGROUND: Primary stability and micromotion of the implant fixture is mostly influenced by its macrodesign. PURPOSE: To assess and compare the peri-implant stress distribution and micromotion of two types of immediate loading implants, immediate loaded screw (ILS) Nisastan and Xive (DENTSPLY/Friadent, Monnheim, Germany), and to determine the best macrodesign of these two implants by finite element analysis. METHODS: In this experimental study, the accurate pictures of two fixtures (ILS: height = 13, diameter = 4 mm and Xive: height = 13, diameter = 3.8 mm) were taken by a new digital camera (Nikon Coolpix 5700 [Nikon, Japan], resolution = 5.24 megapixel, lens = 8x optical, 4x digital zoom). Following accurate measurements, the three-dimensional finite element computer model was simulated and inserted in simulated mandibular bone (D(2)) in SolidWorks 2003 (SolidWork Corp., MA, USA) and Ansys 7.1 (Ansys, Inc., Canonsburg, PA, USA). After loading (500 N, 75 degrees above horizon), the displacement was displayed and von Mises stress was recorded. RESULTS: It was found that the primary stability of ILS was greater (152 microm) than Xive (284 microm). ILS exhibited more favorable stress distribution. Maximum stress concentration found in periapical bone around Xive ( approximately 30 MPa) was lesser than Nisastan ( approximately 37 MPa). CONCLUSIONS: Macrodesign of ILS leads to better primary stability and stress distribution. Maximum stress around Xive was less.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".