The Impact of Implant Angulation on the Stress Distribution and Survival Rate of Implant-Supported Fixed Dental Prostheses: A Retrospective Study
Bibliographic record
Abstract
Background Implant-supported fixed dental prostheses (FDPs) have become a reliable method for the rehabilitation of edentulous patients, offering improved contour, function, esthetics, and overall oral health. This retrospective study aimed to evaluate the impact of implant angulation on the stress distribution and survival rate of implant-supported FDPs using finite element analysis (FEA). Methods A retrospective cross-sectional design was employed, utilizing existing patient records and radiographic data. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for transparent and comprehensive reporting. Sample size calculation was based on a reference study, considering a standard deviation of 2.5 for stress distribution measurements and a minimum detectable effect size of 1.0. Data collection included demographic and clinical characteristics, implant selection and placement details, prosthetic design and fabrication, as well as stress distribution analysis using FEA. Results The study included a total of 307 participants who met the inclusion criteria. Demographic variables demonstrated a balanced gender distribution (p = 0.172), with 51.5% males and 48.5% females. Smoking status (p < 0.001) and income level (p = 0.026) were significantly associated with the research outcomes. Implant characteristics analysis revealed three main types: NobelReplace Select (53.6%), Straumann Bone Level (31.9%), and BioHorizons Tapered Internal (14.5%). Implant type (p < 0.001), length (p = 0.003), diameter (p = 0.019), and manufacturer (p < 0.001) were all found to have statistically significant associations with the research outcomes. Conclusion The findings of this retrospective study highlight the importance of implant angulation on the stress distribution and survival rate of implant-supported FDPs. The evaluation of stress distribution patterns and the analysis of implant characteristics provide valuable insights for optimizing implant design and placement strategies.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".