Systematic review and meta-analysis on marginal bone loss of dental implants placed in augmented or pristine bone sites: Findings from clinical long-term studies
Bibliographic record
Abstract
OBJECTIVES: Bone augmentation can be a risk factor for complications in dental implant therapy. Aim of this systematic review and meta-analysis was to assess the marginal bone loss (MBL) between dental implants placed in augmented or pristine bone sites. STUDY SELECTION: Four electronic databases (PubMed, Embase, Scopus, and Web of Science) were searched for clinical studies with a follow-up period of at least five years. Additionally, a hand search was performed. Risk of bias was assessed using the Newcastle-Ottawa-Scale for non-randomized studies and Cochrane risk-of-bias-tool-2 for randomized controlled trials. Random-effect meta-analysis was performed for the mean MBL at implant level after 5 years of loading for dental implants placed in pristine and augmented bone sites. The study was registered in PROSPERO: CRD42024615716. SOURCES: A total of 11 sources reporting on 10 studies were included. Four studies examined dental implants placed in pristine bone sites, four in augmented bone sites, and two in both conditions. DATA: Random-effect meta-analysis for pristine bone sites estimated a mean MBL of 0.79 mm (95 % CI: 0.32-1.26) and for augmented bone sites a mean MBL of 1.90 mm (95 % CI: 1.73-2.07) after five years with a high heterogeneity of MBL reported in included studies. CONCLUSIONS: Pre-implant augmentation appears to be one but not the only risk factor for MBL. Future studies on implant complications should describe in detail the patient (e.g., adherence, periodontitis history) and local factors (e.g., cleanability of superstructure, attached mucosa) in order to identify further risk factors in the long term. CLINICAL SIGNIFICANCE: Dental implants in augmented bone sites show greater MBL after five years compared to pristine bone sites. Augmentation may increase early bone remodeling. Clinicians should consider augmentation-related remodeling dynamics and patient-specific risk factors when planning implant therapy to optimize long-term outcomes and reduce complications associated with peri‑implant bone loss.
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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 | 0.024 | 0.061 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.044 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".