Minimally Invasive Sinus Augmentation: A Systematic Review
Bibliographic record
Abstract
AIM: Technology improvement and a better understanding of sinus anatomy and wound healing in the past decade have allowed the development of minimally invasive surgical techniques. This systematic review focused on identifying and describing these techniques for vertical and lateral sinus augmentation (VSA and LSA). MATERIALS AND METHODS: Electronic and hand search were conducted to screen the literature published from January 2003 to May 2024. The selected studies had to include detailed techniques for minimally invasive SA. Data extraction included the study types, sample size, technique/instrument details, and outcome measurements. RESULTS: A total of 36 articles (27 VSA, 8 LSA with an additional 1 article included both procedures) with 2732 sinus augmentation met the inclusion criteria. Minimally invasive VSA includes the use of modified rotary instruments with stopper, balloon, hydraulic pressure, digital planning, endoscope, and operating microscope. These techniques aim for conservative flap reflection, precise sinus window preparation, and/or controlled sinus membrane elevation. Most of the selected studies (n = 15) did not report the incidence of sinus membrane perforation. CONCLUSION: Within the limitations of this review, minimally invasive VSA and LSA achieved sufficient sinus augmentation and implant success with the potential advantages of reduced surgical complications and morbidity. Comparative studies with defined outcomes are encouraged to further validate these useful minimally invasive techniques for SA.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".