Comparative Analysis of Guided Bone Regeneration and Allograft Materials in Periodontal Implant Treatment :A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Guided Bone Regeneration (GBR) treatment with allograft materials finds regular use in periodontal implant procedures to rebuild the alveolar bone structure and boost implant success rates. This systematic review and meta-analysis aimed to evaluate the effectiveness of GBR with allografts versus other implant approaches to identify the most effective periodontal treatment. Methods: This systematic review and meta-analysis were conducted according to the PRISMA 2020 guidelines. The literature retrieval was conducted using PubMed, Scopus, Web of Science, and Google Scholar up to May 2025. The relevant studies compared guided bone regeneration (GBR). The Cochrane tool and Newcastle-Ottawa Scale (NOS) were used to determine risk of bias for observational studies and RCTs, respectively. The RevMan 5.4.1 was deployed to conduct meta-analyses, and pooled estimates were obtained using a random-effect model. Heterogeneity was determined by the I2 statistic. GRADE framework was used for the certainty of evidence. Results: 9 studies (5 RCTs, 3 observational, and 1 retrospective study) were included. Vertical/horizontal bone gain and peri-implant probing depth were the primary outcomes. Meta-analysis revealed significant bone gain results on GBR (SMD: 0.83, 95% CI: 0.31-1.34) and no significant difference on probing depth (SMD: 1.23, 95% CI: -0.75-3.22). Most studies had a low risk of bias. The probing depth had significant heterogeneity (I2 = 86%), whereas bone gain heterogeneity was not present (I2 = 0%). Discussion: Periodontal implant stability improves through GBR treatment beyond the use of allograft procedures, which still show effective results in clinical practice.
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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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.049 |
| 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 | 0.003 | 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".