Marginal Bone Loss Around the Implant: A Retrospective Analysis of Bone Remodeling Over Five Years of Follow-Up
Bibliographic record
Abstract
INTRODUCTION: Bone remodeling around implants in implant-supported rehabilitation is a continuous debate with no consensus in the literature. This study aimed to investigate the implant- and patient-specific factors contributing to marginal bone loss near the implant. MATERIALS AND METHODS: We included patients who had implant-supported prosthetic rehabilitation using one implant system, between 2014 and 2018, who had full follow-up documentation and orthopantomography over five years, and who had no unwell-controlled systemic pathologies that may influence bone metabolism. RESULTS: Eighty-one patients who received 500 implants met the inclusion criteria. We observed approximately 1 mm of bone resorption at the five-year follow-up, with the first 0.78 mm of them being documented at the three-year follow-up. Adults younger than 60 years old had an increase in bone resorption by approximately 30%. No difference was seen between men and women. However, a slight increase in bone resorption at five years was seen in female patients older than 50 years old than in ones younger than 50 years (by 30%). The narrowest diameter (3.5 mm; p = 0.001) and anterior mandible (p = 0.008) had the highest bone resorptions. Contrarily, with an insertion depth of approximately 1 mm (p = 0.004), the splinted implant prosthesis (p = 0.21) and zirconia material of the prosthesis (p = 0.57) had the lowest bone remodeling. Moreover, patients younger than 60 years and female patients above 50 years had an increased bone resorption. CONCLUSIONS: Bone remodeling is a multifactorial process. The treatment planning has to take into consideration both implant- and patient-specific factors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".