Magnesium Resorbable Membrane for Guided Bone Regeneration in Critical Size Defect Model in Rabbits—Histomorphometric Analysis
Bibliographic record
Abstract
ABSTRACT Objectives To evaluate the effectiveness of a Mg‐based membrane as a barrier for guided bone regeneration in rabbits calvaria. Materials and Methods Nine rabbits had four critical size defects created in each calvarium, randomly filled with a blood clot or bovine xenograft. One side was covered with the Mg‐based membrane, and the control was left without membrane. After 8 weeks, histomorphometric analysis was performed to compare new bone formation with the pristine bone. Results Mg‐supported membrane for guided bone regeneration (GBR) is safe and promotes bone formation in critical size defects (CSD) in a rabbit calvaria. Gas accumulation was observed in a third of the specimens due to membrane degradation. Histomorphometric analysis revealed greater bone formation in the defects grafted with the Mg membrane (4.85 ± 1.73 mm2) compared to the blood clot (2.14 ± 2.22 mm2). Treating lesions with filler or the magnesium membrane resulted in significantly higher bone formation in all three examined regions (25%–62%). New bone formation was observed beyond the original bone envelope. Conclusion Mg‐based membrane supports guided bone regeneration (GBR), and when used in combination with a bone graft, enhances the performance despite the gas accumulation associated with membrane degradation. Clinical Relevance Limited data exist on the use of Mg‐based membranes in GBR and its effect on bone formation.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".