Clinical efficacy of two vertical soft tissue augmentation techniques for peri‐implant crestal bone level stability: A randomized clinical trial
Bibliographic record
Abstract
Abstract Objectives This study aimed to compare the efficacy of two techniques—acellular dermal matrix (ADM) grafting and tenting technique (TT)—for soft tissue height (STH) augmentation simultaneous to implant placement to minimize peri‐implant crestal bone level (CBL) changes. Methods Forty patients with a healed single mandibular posterior edentulous site with a thin soft tissue phenotype were enrolled. Twenty patients received simultaneously to implant placement ADM grafting, while the others received submerged healing abutment (TT). Clinical peri‐implant soft tissue height and radiographic CBL changes were measured at restoration delivery and 1‐year follow‐up. Results Both techniques effectively increased soft tissue thickness, resulting in a final average STH of 3.4 ± 0.5 mm after augmentation. On average, soft tissue increased by 1.6 ± 0.5 mm in group ADM and by 1.8 ± 0.4 mm in group TT after augmentation. In Group ADM, mesial CBL decreased from 0.4 ± 0.3 mm to 0.1 ± 0.2 mm, and distal CBL decreased from 0.5 ± 0.3 mm to 0.2 ± 0.3 mm over 1 year. In Group TT, mesial CBL remained stable at 0.3 ± 0.2 mm, while distal CBL reduced slightly from 0.5 ± 0.5 mm to 0.3 ± 0.2 mm. Both groups showed minimal changes in CBL, indicating great stability (pmesial = 0.003, pdistal = 0.004). TT was particularly effective in preventing mesial bone loss (pmesial = 0.019). The mesial CBL changes significantly differed between groups (p = 0.019), and not significantly at distal sites (p = 0.944). Neither treatment exhibited significant bone remodeling below the implant shoulder. Conclusion This study suggests that both techniques were successful in STH augmentation, and they may effectively reduce peri‐implant crestal bone level changes, with TT being slightly superior. TT was more prone to post‐surgical complications. This RCT was not registered before participant recruitment and randomization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".