Impact of Two Flap Advancement Techniques and Periosteal Suturing on Graft Displacement During Guided Bone Regeneration
Bibliographic record
Abstract
OBJECTIVES: This preclinical ex vivo porcine study aimed to evaluate the effects of two flap advancement techniques and periosteal suturing (PS) on graft material displacement during primary wound closure in guided bone regeneration (GBR). Secondary objectives included assessing flap advancement and the impact of soft tissue characteristics on graft displacement. MATERIALS AND METHODS: Standardized two-walled horizontal bone defects were created in second premolar sites of pig hemimandibles. Sites were randomized to using either full-thickness flaps with modified periosteal releasing incisions (MPRI) or combination flaps using the mucosal detachment technique (MDT), both with and without PS. Cone-beam computed tomography was used to measure changes in graft material thickness (GMT) at seven incremental levels (L0-L6) relative to the implant platform, before and after primary wound closure. Keratinized mucosa width (KMW), flap thickness (FT), and flap advancement (FA) were also recorded. RESULTS: Sixty-eight horizontal bone augmentation procedures were performed on 34 pig hemimandibles, divided into four groups (MDT+PS, MDT-PS, MPRI+PS, MPRI-PS). Mean overall change in GMT at L0 was -24.5% ± 14.0% for MPRI and - 23.0% ± 14.3% for MDT (p ≥ 0.085). PS reduced graft displacement (-14.2% ± 11.5%) compared with no PS (-33.2% ± 16.9%, p < 0.001). FA was 8.3 ± 1.1 mm (MPRI) and 8.3 ± 1.5 mm (MDT). The mean KMW was 6.8 ± 0.9 mm, and FT ranged from 0.8 to 1.6 mm. CONCLUSIONS: PS significantly reduced graft material displacement during primary wound closure, while flap advancement techniques and soft tissue characteristics had no impact on graft stability. Both surgical techniques provided sufficient flap advancement for primary wound closure.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".