Marginal Bone Level Changes in Full‐Arch Rehabilitation: Digital Versus Analog Protocols—A 5‐Year Retrospective Study
Bibliographic record
Abstract
INTRODUCTION: This retrospective study compares the clinical outcomes of analog impressions versus intraoral scanning in full-arch immediate loading rehabilitations. Specifically, it evaluates peri-implant marginal bone level (MBL) changes at different time intervals (implant placement, loading, and at 2 and 5 years), as well as rates of mechanical and prosthetic complications. MATERIALS AND METHODS: The study included 62 patients who underwent full-arch rehabilitation with immediate implant placement between 2019 and 2020. Patients were divided into two groups: analog impression and digital intraoral scanning. All patients were rehabilitated with fixed titanium-PMMA screw retained restorations. Bone level was assessed through standardized intraoral radiographs at key time points. Additional parameters recorded included procedural time, prosthetic complications, and implant failures. Statistical analyses involved repeated measures ANOVA and post hoc Bonferroni tests. RESULTS: The follow-up period was 5 years. Implant survival was 99.6%. No significant differences were found in prosthetic complications. MBL was slightly higher in the analog group at baseline (mean = 0.21, SD = 0.04 vs. digital mean = 0.17, SD = 0.04, t-test p-value < 0.001) than in the digital group. Despite this, the overall bone loss remained within clinically acceptable limits during the follow-up period. Digital impressions significantly reduced procedural time compared to analog methods. CONCLUSIONS: Both impression techniques provided satisfactory clinical outcomes. Digital impressions demonstrated efficiency advantages but were associated with slightly greater bone loss over time. Analog impressions remain a reliable standard for full-arch immediate loading rehabilitations, though digital methods show promise for improved patient experience. Further randomized, long-term studies are needed. CLINICAL SIGNIFICANCE: Digital impressions offer a faster and more comfortable workflow for full-arch immediate loading rehabilitations, potentially improving patient compliance. However, their association with slightly greater bone loss warrants further investigation to optimize long-term stability.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".