Osseodensification technique in crestal maxillary sinus elevation—A narrative review
Bibliographic record
Abstract
Osseodensification is a novel approach that has significantly advanced the field of implant dentistry, particularly in the context of transcrestal maxillary sinus floor elevation. This technique involves the use of specially designed burs that compact and densify bone along the osteotomy walls, thereby enhancing implant primary stability and facilitating osseointegration in low-density bone. This article reviews the historical evolution of implant site preparation, and the biomechanical, histological, and clinical evidence of osseodensification with a special focus on its application in sinus floor augmentation. The integration of this technique into contemporary practice represents a paradigm shift, offering a minimally invasive and efficient solution for addressing the challenges of posterior maxilla, with improved patient-reported outcomes and low complication rate. Three different protocols for sinus lift and implant placement using osseodensification burs are proposed based on available literature, and risk factors for Schneiderian membrane perforation based on residual bone height are discussed, along with implant-related outcomes and patient-reported outcome measures. The potential for osseodensification to become a standard practice in sinus floor augmentation is emphasized, highlighting key aspects such as surgical protocol and patient selection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".