20 years of alveolar distraction: A systematic review of the literature
Bibliographic record
Abstract
BACKGROUND: The Vertical Alveolar Distraction Osteogenesis (VADO) technique is an excellent solution for bone and soft tissue neoformation in areas in which there has been significant bone atrophy that hinders normal rehabilitation using dental implants. The goal of this systematic review is to analyze the most relevant articles published on VADO in the literature over the past 20 years. MATERIAL AND METHODS: The review was performed by using the keywords: "alveolar ridge", "distraction ostegenesis" and "dental implant". This search produced a total result of 240 articles. The clinical studies and cases reported in humans amounted to 113 articles, 18 articles referred to studies developed on animals and 33 review articles. The presentation of this systematic analysis follows the criteria described in the PRISMA declaration. RESULTS: 22 articles complied with the inclusion criteria and 7 articles more were added manually, reaching a total sample of 29 studies. Following the analysis of the studies, they were classified into 18 high-quality, 10 mediumquality and 1 low-quality study. Only 4 studies achieved a maximum score of 9 (according to NewCastle Ottawa Scale, NOS). CONCLUSIONS: VADO is a technique with greater potential in vertical gain. The performance of dental implants has a success and survival rate similar to dental implants placed on bones that are not subject to increase techniques.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".