Horizontal ridge augmentation using native collagen membrane vs titanium mesh in atrophic maxillary ridges: Randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: Several techniques have been proposed to reconstruct deficient alveolar ridges including bone blocks, ridge splitting and guided bone regeneration (GBR). GBR has been successfully established in restoring horizontal bone deficiency. However, yet still there is a debate regarding the ideal barrier for GBR. PURPOSE: To evaluate the quantity and the quality of the bone gained using collagen membrane with 1:1 mixture of autogenous and anoraganic bovine bone mineral compared to titanium mesh with the same mixture of bone for GBR of horizontally deficient maxillary ridges. MATERIALS AND METHODS: Two different grafting techniques were evaluated, 10 patients receiving GBR using native collagen membrane using 1:1 autogenous and anorganic bovine bone mineral (ABBM) bone mixture, and 10 patients receiving GBR using titanium mesh with same mixture of bone. RESULTS: Statistical analysis showed a significant increase in alveolar bone width in both techniques with a mean bone gain of 4.0 mm for Collagen group and 3.7 mm for titanium mesh group. Bone area percent was almost 28% for both groups. For Ti-mesh group, six sites soft tissue healing was uneventfully with no signs of wound dehiscence. However, four cases showed mesh exposure first 3 patients showed this exposure 3 weeks postoperatively while the fourth patient showed exposure 4 months postoperatively. The mean graft resorption in the Collagen and mesh group 6 months postoperative was considered nonsignificant. CONCLUSIONS: GBR with both collagen membrane and titanium mesh using a 1:1 mixture of autogenous and ABBM is a viable technique for horizontal augmentation of deficient maxillary alveolar ridges. Titanium mesh is a more technique sensitive compared to collagen membrane. Soft tissue dehiscence and difficulty during second stage removal should limit its use in augmentation of horizontally deficient maxillary ridges.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".