The influence of wound closure on graft stability: An in vitro comparison of different bone grafting techniques for the treatment of one‐wall horizontal bone defects
Bibliographic record
Abstract
OBJECTIVES: The aim of this preclinical study was to analyze and compare different grafting techniques with respect to volume stability after wound closure. MATERIALS AND METHODS: Four different grafting techniques were evaluated in vitro for volume stability in a one-wall horizontal defect configuration. Group 1: guided bone regeneration (GBR) with collagen membrane, particulated xenograft; group 2: GBR with collagen membrane, particulated xenograft, pins; group 3: GBR with titanium-reinforced membrane, particulated xenograft, pins; group 4: autogenous block graft, particulated xenograft, collagen membrane. Cone beam computed tomography scans were performed before and after wound closure, and the horizontal bone dimensions were analyzed for stability at 0-5 mm apical to the implant shoulder (H0-H5). RESULTS: At H0-H2, wound closure induced a statistically significant change in bone dimensions in groups 1 and 2. In group 3, only the change in H0 was significant, and for group 4, only H2 was significant. CONCLUSION: Wound closure has a significant impact on graft stability in a one-wall horizontal defect configuration. GBR with additional membrane fixation showed better results than without. Titanium-reinforced membranes and autogenous blocks showed significantly greater volume stability than GBR with collagen membrane, especially in the coronal portion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".