The impact of deproteinized bovine bone particle size on histological and clinical bone healing outcomes in the augmented sinus: A randomized controlled clinical trial
Bibliographic record
Abstract
Abstract Objective The effect of different deproteinized bovine bone mineral (DBBM) particle sizes on bone healing in maxillary sinus floor augmentation remains unclear. This study compared the newly formed tissue and angiogenesis‐related bone healing after sinus floor augmentation using large or small DBBM particles. Materials and Methods Overall 32 patients were randomly divided into two groups using either large (1–2 mm) or small (0.25–1 mm) DBBM particles for sinus floor augmentation. After 6 months, the mineralized tissue volume was calculated using micro‐computed tomography (micro‐CT) analysis. The newly formed tissue composition was histomorphometrically analyzed. Angiogenesis was also examined by means of vascular endothelial growth factor (VEGF) expression. Implant failure and marginal bone loss were measured at a 1‐year follow‐up. Statistical analysis was performed using independent samples t‐test. Results Micro‐CT analysis demonstrated that grafting with large particles resulted in higher bone volume (6.99 ± 2.72 mm3, p = 0.002) and Bone Volume/Tissue Volume (0.25 ± 0.1, p = 0.03) compared with small particles (3.76 ± 1.83 mm3 and 0.14 ± 0.13, respectively). Small particles showed higher non‐mineralized tissue volume (26.31 mm3) compared with large particle group (17.4 ± 5.34 mm3) with p = 0.001. The histological data revealed significantly higher area of newly formed bone (32.15% ± 14.04% for the large particle and 15.99% ± 14.12% for the small particle groups, p = 0.004). Likewise, non‐mineralized tissue was significantly greater in the small particle group (66.48% ± 20.97%) compared with the large particle group (44.36%, p = 0.016). Moreover, use of large particles resulted in a significantly higher VEGF staining intensity score and VEFG positive cells. No implant failure was recorded in both groups, while no difference was found in terms of marginal bone loss at the 1‐year follow‐up. Conclusions Sinus floor augmentation using large DBBM particles resulted in more angiogenesis expression, higher bone volume, and new bone formation at 6 months after sinus augmentation. However, clinical outcomes with regards to implant placement were similar in both groups.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".