Application of Biologics in Maxillary Sinus Augmentation Surgery: A Narrative Review
Bibliographic record
Abstract
Maxillary sinus floor augmentation (MSFA) is one of the most predictable hard tissue augmentation procedures performed to support long-term dental implant survival and success. However, risks and complications still exist with this procedure including pain, bleeding, infection, oroantral communication, and inadequate bone regeneration for implant placement. To decrease some of these potential complications and improve outcomes, biologics are becoming more widely used in maxillary sinus augmentation. Autologous blood concentrates (ABCs) can be utilized as a membrane to seal a sinus membrane perforation or as "sticky bone" to mix with particulate material to make a congealed bone graft that will stay in place and be less likely to migrate from the site of placement or into the sinus cavity. Although the data is heterogeneous and somewhat conflicting, there is some evidence to support the use of ABCs for bone graft consolidation and overall wound healing due to the growth factors contained in the ABC. In addition, clotting factors in the plasma aid in hemostasis, which is an essential first step in the wound healing cascade. Individual growth factors such as rhBMP-2 and rhPDGF-BB are also clinically available for use in bone grafting procedures such as MSFA. rhBMP-2 is FDA approved for maxillary sinus augmentation, and clinical trials demonstrate de novo bone formation to facilitate dental implant placement. However, studies do not show a significant improvement over autogenous bone or bone substitutes such as xenograft or allografts. rhPDGF-BB has limited studies in this area, but may decrease residual particulate graft material and aid in increasing vital bone when bone substitutes are used. However, the use for MSFA is "off label" and few studies are available. Finally, enamel matrix derivative (EMD) has no data, and therefore, limited use for MFSA.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".