The influence of vertical ridge augmentation techniques on peri‐implant bone loss: <scp>A</scp> systematic review and meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: The primary aim of this systematic review was to investigate and compare the outcomes of different vertical ridge augmentation (VRA) techniques in relation to peri-implant bone loss (PBL), after at least 12 months of functional loading. MATERIAL AND METHODS: The search was conducted to find all the studies about VRA and measurements of PBL with at least 12 months follow-up. Three pairwise meta-analysis (MA) was performed to completely evaluate the outcomes. RESULTS: A total of 42 studies were included, of which 11 were randomized clinical trials (RCTs). RCTs were available only for guided bone regeneration (GBR), onlay, and inlay techniques. The weighted mean estimate (WME) of PBL value was found to be 1.38 mm (95% confidence interval [95% CI]: 1.10-1.66) after a mean follow-up of 41.0 ± 27.8 months. GBR, Inlay, Onlay, osteodistraction, and SBB represented in weight 32.9%, 30.6%, 25.0%, 7.6%, and 3.9%, respectively; and their WME (95% CI) were 1.06 (0.87-1.26) mm, 1.72 (1.00-2.43) mm, 1.31 (0.87-1.75) mm, 1.81 (0.87-1.75) mm, and 0.66 (0.55-0.77) mm, respectively. Among the secondary outcomes, the analysis was conducted for vertical bone gain, healing complication rate, surgical complication rate, implant survival, and success rate. CONCLUSIONS: The primary findings of the meta-analysis, based on the changes between final and baseline values, showed that the peri-implant bone loss could be influenced by the type of intervention but there is a need to evaluate in RCTs the behavior of the peri-implant bone levels after long-term follow-up for all techniques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.027 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".