Lateral Ridge Augmentation Using Autogenous Block Grafts and Guided Bone Regeneration: A 10‐Year Prospective Case Series Study
Bibliographic record
Abstract
BACKGROUND: The use of autogenous block grafts harvested from intraoral donor sites has proven to be effective for the reconstruction of horizontal bone defects. PURPOSE: The objective of this study was to analyze implant success and the rate of block graft resorption 10 years after ridge augmentation to elucidate contributing factors influencing graft maintenance. MATERIALS AND METHODS: A staged horizontal block graft augmentation was performed in 52 implant sites exhibiting severe horizontal bone atrophy using autogenous block grafts protected by DBBM and collagen membranes. The crest width was assessed intraoperatively at surgery and at re-entry after 6 months. At the 10 year reexamination clinical and radiographic parameters were assessed using cone beam computed tomography. RESULTS: The 10-year implant success rate amounted to 98.1%, with minimal peri-implant bone loss (-0.17 mm for the maxilla, -0.09 mm for the mandible). The surface resorption rate after 10 years was 7.7% (0.38 mm). Grafts originating from the chin demonstrated significantly better graft maintenance at 10 years compared to retromolar grafts. Recipient site and age had no significant impact on graft resorption, whereas females showed more bone loss at the 10-year examination. CONCLUSIONS: Lateral ridge augmentation using autogenous block grafts and guided bone regeneration demonstrated a favorable success rate of 98.1% with minimal block graft resorption of 7.7% after 10 years. Modulating factors were origin of the graft and gender.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".