Effect of Crestal Position on Bone–Implant Stress Interface of Three-Implant Splinted Prostheses: A Finite Element Analysis
Bibliographic record
Abstract
Optimizing stress distribution at the bone-implant interface is critical to enhancing the long-term biomechanical performance of dental implant systems. Vertical misalignment between splinted implants can result in elevated localized stresses, increasing the risk of material degradation and peri-implant bone resorption. This study employs three-dimensional finite element analysis (FEA) to evaluate the mechanical response of peri-implant bone under oblique loading, focusing on how variations in vertical implant platform alignment influence stress transmission. Four implant configurations with different vertical placements were modeled: (A) all crestal, (B) central subcrestal with lateral crestal, (C) lateral subcrestal with central crestal, and (D) all subcrestal. A 400 N oblique load was applied at 45° simulated masticatory forces. Von Mises stress distributions were analyzed in both cortical and trabecular bone, with a physiological threshold of 100 MPa considered for cortical bone. Among the models, configuration B exhibited the highest cortical stress, exceeding the physiological threshold. In contrast, configurations with uniform vertical positioning, particularly model D, demonstrated more favorable stress dispersion and lower peak values. Stress concentrations were consistently observed at the implant-abutment interface across all configurations, identifying this area as critical for design improvements. These findings underscore the importance of precise vertical alignment in implant-supported restorations to minimize stress concentrations and improve the mechanical reliability of dental implants. The results provide valuable insights for the development of next-generation implant systems with enhanced biomechanical integration and material performance under functional loading.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".