Histological and histomorphometric analysis of bone tissue after guided bone regeneration with non‐resorbable membranes vs resorbable membranes and titanium mesh
Bibliographic record
Abstract
BACKGROUND: Guided bone regeneration (GBR) allows to achieve vertical ridge augmentation whether with nonresorbable membranes or resorbable membranes with Ti-mesh, but till now no studies are published comparing histological and histomorphometrical outcomes of these two procedures. MATERIALS AND METHODS: Forty partially edentulous patients required vertical bone regeneration to place implants in the posterior mandible: 20 patients were randomly assigned to group A (Ti-PTFE); while 20 patients to group B (Collagen plus Ti-mesh). For both groups, graft material was a 50:50 mixture of autogenous bone and bone allograft. After 9 months, tissue biopsies were taken from augmented sites (regenerated bone ROI-1; native bone ROI-2) and undergone to histological and histomorphometric analysis. Percentages of bone tissue (B.Ar), biomaterial (Mat. Ar), and soft tissue (St.Ar) were measured; measurements of perimeters were calculated too. ROI-1 values were also compared to ROI-2 in both groups. RESULTS: Twenty-five samples were collected and analyzed consecutively: 13 in group A and 12 in group B. The mean B.Ar, Mat.Ar, and St.Ar were 39.7%, 8.6%, and 52.1% in group A; similar results were obtained in group B, with mean values of 42.1%, 9.6%, and 48.3%, respectively. No significant statistically differences were observed. Differences were observed between ROI-1 and ROI-2 in both group. Finally, bone structure index of ROI-1 and ROI-2 showed statistical differences. CONCLUSIONS: The preliminary results of this study suggest that GBR using nonresorbable membranes and Ti-mesh with resorbable membranes in combination with autogenous bone and bone allograft provide similar histological and histomorphometric results.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".