Resorbable scaffolds for alveolar ridge augmentation procedures: A systematic review
Abstract
Purpose: To systematically review the efficacy and safety of resorbable scaffolds for bone augmentation of alveolar bone defects.
Methods: A specific PICO question was formulated: In partially or fully edentulous patients requiring alveolar ridge augmentation (P), how do resorbable scaffolds (I), compared with non-resorbable devices (e.g., titanium meshes) or conventional bone augmentation techniques (C), perform in terms of clinical outcomes including bone gain, implant loss and success, and complication rates (O)? A PubMed/MEDLINE electronic search was conducted to identify clinical studies published until December 2025. No restrictions on publication date were imposed, and only studies published in the English language were considered. Selected studies were all studies on humans using resorbable devices for alveolar ridge augmentation; in vivo and in vitro studies were excluded. Study characteristics, operative techniques, materials, and clinical outcomes including complications, bone augmentation techniques, implant stability, survival and success rates were extracted and analyzed. Resorbable materials evaluated included polylactide (PLA), polycaprolactone (PCL), Polylactic Acid-Polyglycolic Acid (PLGA), and beta-tricalcium phosphate (β-TCP).
Results: A total of 3704 articles were found, but only 7 studies met the pre-established inclusion criteria and were considered suitable for analysis. These studies included 39 patients and 45 edentulous ridges. Implant loss was…
How strong is the evidence?
Evidence Hub reads this as a systematic review, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 39 participants or records.
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.