Long‐Term Follow‐Up on Soft and Hard Tissue Levels Following Guided Bone Regeneration Treatment in Combination with a Xenogeneic Filling Material: A 5‐Year Prospective Clinical Study
Bibliographic record
Abstract
PURPOSE: In the present prospective study, bone augmentation by guided bone regeneration (GBR) in combination with bovine hydroxyapatite (BHA) as filling material was evaluated with regard to soft and hard tissue stability over time. MATERIALS AND METHODS: Implant survival, radiologic bone level (marginal bone level [MBL]), and clinical soft tissue parameters (marginal soft tissue level [MSTL]) were observed. Twenty patients received a total of 41 implants (Brånemark System, Nobel Biocare, Göteborg, Sweden) in conjunction with GBR treatment. The end point of the study was after 5 years following implant placement. RESULTS: The cumulative implant survival rate was 97.5% corresponding to one implant failure. The radiologic evaluation of the MBL demonstrated a crestal bone height above the level of the fixture head. The bone height decreased from -3.51 to -2.38 mm (p < .001). The MSTL was -1.52 mm at baseline and -1.15 mm at the 5-year follow-up (p < .04) demonstrating a stable submucosal crown margin throughout the study period. CONCLUSION: GBR treatment in combination with a xenogeneic filling material (BHA) is a viable treatment option in order to maintain stable hard and soft tissue levels in conjunction with augmentative procedure related to oral implant treatment.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".