"Xenogeneic Bone Block in Lateral Ridge Augmentation: Where Are We Now?” A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: To investigate the clinical outcomes of xenogeneic bone blocks (XBBs) used for lateral ridge augmentation, specifically focusing on bone gain, graft survival, and implant survival. MATERIALS AND METHODS: Data searches were conducted in PubMed, Embase, and ClinicalTrials.gov for randomized controlled trials (RCTs) and prospective cohort studies up to March 1, 2024. Horizontal bone gain (HBG), horizontal bone resorption (HBR), graft survival rates, and implant survival rates were analyzed. The Cochrane Risk of Bias Tool 2 (RoB2) and Newcastle-Ottawa Scale (NOS) were applied to assess the quality and risks of the included studies. RESULTS: Four RCTs and five prospective cohort studies comprised a total of 120 bone graft sites and 141 implants that were included in the meta-analysis. A noncomparative analysis resulted in a weighted mean HBG of 4.38 mm and HBR of 0.85 mm. Comparative analysis with data from four RCTs compared XBBs with autogenous bone blocks (ABBs). The analysis resulted in a statistically significant greater HBG in XBBs, with a mean difference of 0.72 mm (95% CI = 0.067 to 1.382, P = .031, I2 = 28.2%). The weighted graft survival rate for XBBs was 91.3% (95% CI = 76.6% to 97.1%, I2 = 58.0 %), and the weighted implant survival rate was 84.3% (95% CI = 72.6% to 91.6%, I2 = 31.6 %). Histologically, the mean percentage of mineralized vital bone in XBBs ranged from 11.6% to 29.8%, and the resorption rate ranged from 7.3% to 21%. CONCLUSIONS: The use of XBBs for lateral ridge augmentation demonstrates an acceptable survival rate and yields an adequate bone volume for subsequent implant therapy. However, the survival rate of implants placed in ridges augmented with XBBs is less favorable when compared to those augmented with ABB grafts.
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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 | 0.021 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.034 |
| Bibliometrics | 0.006 | 0.006 |
| 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".