Alveolar Ridge Reconstruction with Titanium Mesh and Autogenous Particulate Bone Graft: Computed Tomography‐Based Evaluations of Augmented Bone Quality and Quantity
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to evaluate the quality and quantity of augmented bone following alveolar ridge reconstruction with titanium mesh and autogenous particulate bone graft for implant placement in terms of the preoperative bone defect. MATERIALS AND METHODS: Forty-one patients (50 sites) rehabilitated between September 2000 and May 2009 with autogenous particulate intraoral bone or iliac cancellous bone marrow grafts and micro-titanium meshes were enrolled. We classified the bone defects by means of shape as complex horizontal-vertical (HV), horizontal (H), and socket (S) types, and the augmented bone was evaluated based on preoperative computed tomographic data. The postsurgical complications were assessed during the healing period and after implant superstructure placement. RESULTS: The bone defects were successfully augmented using the titanium mesh technique. The HV-type defect was the most difficult to augment (mean horizontal gain, 3.7 ± 2.0 [SD] mm; mean vertical gain, 5.4 ± 3.4 [SD] mm). The mean horizontal gain with the H-type defect was 3.9 ± 1.9 mm. The S-type defect achieved the most efficient bone augmentation (mean horizontal gain, 5.7 ± 1.4 [SD] mm; mean vertical gain, 12.4 ± 3.1 [SD] mm). The major postsurgical complications were mesh exposure, infection, total or partial bone resorption, and temporary neurological disturbances. Implant failure was observed in one case. The HV-type defect showed significantly higher bone resorption (p < .05) than the other defect types. CONCLUSIONS: Autogenous bone grafting with titanium mesh allows adequate vertical and horizontal alveolar bone reconstruction both quantitatively and qualitatively for implant placement. However, the clinical outcome of augmentation depends on the type of preoperative bone defect.
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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) | 0.000 | 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 | 0.000 |
| 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".