Impact of Buccal Bone Arch Contour on Bone Remodeling and Esthetics in Guided Bone Regeneration: A Retrospective Study
Bibliographic record
Abstract
OBJECTIVES: To assess the stability of hard tissue following simultaneous guided bone regeneration (GBR) in the anterior maxilla, analyze the impact of the buccal bone arch contour on postoperative bone remodeling and restorative outcomes. METHODS: Patients who underwent anterior maxillary implantation and simultaneous GBR were included. Radiographic metrics were evaluated using preoperative, immediate postoperative, and follow-up cone beam computer tomography (CBCT) scans, and esthetic indicators were extracted from follow-up clinical records. The buccal bone arch contour of the edentulous area was reconstructed using the mirror symmetry technique. Implants were grouped based on the relative position of the bone grafts and implant to the contour immediately after surgery for comparisons of radiographic and esthetic outcomes. RESULTS: A total of 66 patients (66 implants) were ultimately included. For simultaneous GBR in the anterior maxilla, a bone gain of approximately 0.55-0.75 units was expected for every additional unit of bone graft. Bone grafts augmented outside the buccal bone arch contour tended to resorb back to the contour, suggesting that excessive bone augmentation may not provide significant benefits. Implants placed more than 3 mm palatally from the contour tended to achieve greater buccal bone wall thickness and more predictable esthetic outcomes. CONCLUSIONS: The buccal bone arch contour provides an individualized reference for determining the appropriate bone graft volume and implant position. Placing the implant at least 2 mm palatally from the contour and augmenting bone grafts to exceed the contour by at least 1 mm appears to be a practical and effective strategy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".