Effect of Dental Implant Cross‐Sectional Design on Cortical Bone Structure Using Finite Element Analysis
Bibliographic record
Abstract
PURPOSE: This finite element analysis investigation evaluated the effect of different implant cross-sectional designs on bone stress levels under different loading patterns. MATERIALS AND METHODS: Finite element analysis program was used to construct four different three-dimensional models describing 4x10-mm implants in blocks of cortical and trabecular bone. A 5-mm-long abutment was modeled above each implant. The implant in model 1 was unthreaded, while in model 2 the implant was circularly threaded. The third implant in model 3 had the cross-sectional shape as a 16-sided star-shaped design. The implant in model 4 was constructed unthreaded, with a diameter of 4.5 mm. Vertical and horizontal loads of 100 N each were applied on the top middle node of each implant assembly. All nodes at the bottom surface of the bone models were restrained. RESULTS: By comparing models 1, 2, and 3, the lowest bone stress values under vertical and horizontal forces were observed around the unthreaded implant in model 1 (8.92 and 94.52 MPa, respectively). The highest stress value under vertical loading was shown around the threaded implant in model 2 (10.07 MPa), whereas the highest stress value under horizontal loading was observed around the star-shaped implant in model 3 (108.40 MPa). Model 4, with a wider unthreaded design, had stress values under vertical and horizontal loading of 7.32 and 71.35 MPa, respectively. CONCLUSIONS: It was concluded that the unthreaded implant design produced the least bone stress. An increase in implant diameter could produce marked reduction in stress value in the bone around the neck 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 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".