Alveolar ridge preservation in the posterior maxilla reduces vertical dimensional change: A randomized controlled clinical trial
Abstract
OBJECTIVES
To test whether or not alveolar ridge preservation reduces vertical changes in the posterior maxilla compared to spontaneous healing following tooth extraction.
MATERIALS AND METHODS
Forty subjects requiring extraction of maxillary posterior teeth with root apices protruding into the maxillary sinus floor were consecutively enrolled. Patients were randomly assigned to either one of two surgical interventions: an alveolar ridge preservation procedure using collagenated bovine bone mineral and a resorbable collagen membrane (test) or no grafting (control). Cone-beam computed tomographies were taken immediately and at 6 months after surgery, prior to dental implant placement.
RESULTS
Based on radiographic data, the level of the sinus floor remained stable over time (baseline to 6 months) in the test group (-0.14 mm [-0.31, -0.02]). In the control group, the sinus floor level shifted more coronally (-1.16 mm [-1.73, -0.61]) than the test group (p < 0.05). The test group demonstrated a significantly larger residual bone height than the control group at 6 months (7.30 mm [6.36, 8.20] vs. 4.83 mm [3.94, 5.76], respectively, p < 0.05). Implant placement without any additional sinus augmentation procedure was performed in 42.9% of test group cases, whereas in all of the subjects in the control group an additional augmentation procedure was needed (100% of the cases).
CONCLUSION
Alveolar ridge preservation in the posterior maxilla maintained the vertical bone height more…
How strong is the evidence?
Evidence Hub reads this as a randomized trial, done in people. Its evidence level is 2 of 5 (High · randomized trial). Randomized trials of a reasonable size are the strongest single-study design.
Read the paper
Related studies
- In silico toxicology protocols
- QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
- Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO2's Chemisorption and Diffusion in Mg-MOF-74
- In-silico strategies in nano-drug design: Bridging nanomaterials and pharmacological applications
- mRNA codon optimization with quantum computers
- The computational power of the human brain
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.