Stabilization Techniques for Soft Tissue Grafting Around Dental Implants: Case Report
Bibliographic record
Abstract
INTRODUCTION: Implants that lack keratinized tissue (KT) have been associated with increased plaque accumulation, gingival inflammation or hue of metal showing through the tissue. Free gingival grafts (FGGs) are a predictable treatment for minimal or lack of KT. FGGs can increase the zone of KT around teeth and implants alike. Despite predictability of FGGs, stabilizing the graft around implants can be challenging, but is critical for success. Little information is available regarding ways to stabilize FGGs around implants. Acrylic or composite stents are a viable option for obtaining graft stability and support during the healing process. CASE PRESENTATION: This case report highlights the practicality of using acrylic or composite stents for FGG stabilization with successful outcomes. Two patients presented with dental implants, with minimal or lack of KT requiring soft tissue augmentation. FGGs were harvested from the palate and fitted around implant carriers allowing stabilization and adequate suturing. Custom-made acrylic or composite stabilization stents were fabricated to fit around implant carriers, which were screwed into the implant platform, and hollowed out internally to provide space for the graft. Postoperative visits showed healthy, stable zones of KT in both cases. CONCLUSION: The customized acrylic or composite stents allowed stabilization of the FGGs with successful outcome.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.012 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".