Histomorphometric and microtomographic analysis of maxillary sinus augmentation surgeries with and without collagen membrane on the lateral window: A randomized clinical trial
Bibliographic record
Abstract
OBJECTIVE: To evaluate the histomorphometric and computerized microtomographic (Micro-CT) analysis of the regenerated bone tissue from maxillary sinus augmentation surgery, with and without using the collagen membrane on the external osteotomy window. MATERIALS AND METHODS: Twelve patients were selected for this prospective, controlled, and randomized study. The patients were submitted to bilateral maxillary sinus surgery in a split-mouth design. On the test side, the maxillary sinus augmentation procedure included using Geistlich Bio-Oss® and a Geistlich Bio-Gide® collagen membrane covering the lateral osteotomy window. On the control side, only Geistlich Bio-Oss® was used without the presence of the membrane. After 6 months, the surgeries for implant installation were performed. In this surgical phase, specimens of the regenerated tissue were collected for histological and Micro-CT analysis. RESULTS: In the histomorphometric evaluation, the mean (±SD) percentages of newly formed bone were 43.9% (±11.5) and 40.8% (±8.9) in the test and control groups, respectively. The corresponding values of the Micro-CT analysis were 36.6% (±3.4) and 37.2% (±4.7) in the test and control groups, respectively. There was no statistically significant difference between the test and control groups in the two methods. In addition, there was no statistically significant difference between the mean percentage of biomaterial remaining between the test and control groups. However, the mean percentage of newly formed bone was significantly higher and the mean percentage of remaining biomaterial was significantly lower in the histomorphometric analysis compared to the values obtained through microtomography. CONCLUSION: The additional use of collagen membranes in maxillary sinus surgery does not offer advantages in newly formed bone.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".