A Literature Review on Biomaterials in Sinus Augmentation Procedures
Bibliographic record
Abstract
BACKGROUND: Sinus augmentation is a common procedure to increase bone volume and allow for proper implant placement in the atrophic posterior maxilla. Although the patient's own bone is considered the best grafting material, various synthetic or bovine-derived alternatives are used to simplify the grafting procedure. PURPOSE: The overall objective of this review was to assess the efficacy of different graft materials used in sinus augmentation procedures as demonstrated in animal studies. MATERIALS AND METHODS: A specific and sensitive database was initially created via PUBMED, focusing on studies published in English peer-reviewed journals between 1995 and 2004 and kept updated until 2006. RESULTS: Twenty-six articles were available for comparison and discussion; none concerned the use of alloplastic materials; 24 were comparative histomorphometric; and two were biomechanical studies. Because of a great variability in study designs, different implant types, great range in follow-up, and lack of specific integration or loading period, a comparison of the studies and the biomaterials used was difficult. CONCLUSIONS: In general, autogenous bone is the most predictable material of choice for augmentation procedures, despite a 40% resorption, because it is highly osteoconductive and less dependent on sinus floor endosteal bone migration. The addition of bovine bone mineral to autogenous bone can be beneficial for graft success because it acts as a slowly resorbing space maintainer. Porous hydroxyapatite is suitable when mixed with autogenous bone because it enhances bone formation and bone-to-implant contact in augmented sinuses. Histological evaluation showed that demineralized freeze-dried bone is inferior to other materials. Within the limitation of the animal studies examined in this review and only based on histological examination, the initial osseointegration of implants seems independent of the biomaterial used in grafting procedures.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".