Hard and soft tissue analysis of alveolar ridge preservation in esthetic zone using deproteinized bovine bone mineral and a saddle connective tissue graft: A long‐term prospective case series
Bibliographic record
Abstract
AIM: Although alveolar ridge preservation (ARP) procedures appear to limit bone resorption after dental extraction, long-term outcomes remain limited. The objective of this prospective case series was to evaluate the long-term hard and soft tissue changes after ARP procedure in the aesthetic area, using deproteinized bovine bone mineral (DBBM) and saddle connective tissue graft. MATERIALS AND METHODS: Fifteen patients were subjected to ARP and impressions and CT scans were taken at baseline and 3 months. After 5 to 7 years, a secondary long-term clinical and radiological analysis was carried out. Horizontal alveolar bone changes, soft tissue profiles and implant outcomes were assessed. RESULTS: Although a limited hard and soft tissue remodeling occurred during the first 3 months after ARP, from 3 months to the long-term evaluation, the alveolar bone dimensions remained stable and the soft tissue profiles significantly increased, in the more cervical levels. The implant survival rate after 5 to 7 years yielded 100% and peri-implant bone levels and soft tissue health were good. CONCLUSION: Within the limits of the study, the present data confirms the long-term effectiveness of ARP using DBBM and a saddle connective tissue graft offering stable hard and soft tissue conditions up to 5 to 7 years.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".