Vertical soft tissue augmentation to treat implant esthetic complications: A prospective clinical and volumetric case series
Bibliographic record
Abstract
INTRODUCTION: Challenging implant esthetic complications are often characterized by implant malpositioning and interproximal attachment loss of the adjacent teeth. However, limited evidence is available on the treatment of these conditions. The aim of this study was to evaluate the clinical, volumetric, and patient-reported outcome following treatment of peri-implant soft tissue dehiscences (PSTDs) exhibiting interproximal attachment loss on adjacent teeth, performed through vertical soft tissue augmentation with implant submersion. METHODS: Ten subjects with isolated PSTD in the anterior maxilla characterized by adjacent dentition exhibiting interproximal attachment loss were consecutively enrolled and treated with horizontal and vertical soft tissue augmentation, involving crown and abutment removal, two connective tissue grafts, and submerge healing. Clinical outcomes of interest included mean PSTD coverage, mean PSTD reduction, clinical attachment level (CAL) gain at the implant and adjacent sites and soft tissue phenotype modifications at 1 year. Optical scanning was used for assessing volumetric changes. Professional assessment of esthetic outcomes was performed using the Implant Dehiscence coverage Esthetic Score (IDES), while patient-reported esthetic assessment involved a 0-10 visual analogue scale. RESULTS: The mean PSTD depth reduction and mean PSTD coverage at 1 year were 2.25 mm, and 85.14%, respectively. A mean keratinized tissue width (KTW) gain of 1.15 mm was observed, while the mean gain in mucosal thickness (MT) was 1.58 mm. A mean CAL gain of 1.45 mm was obtained at the interproximal aspect of the adjacent dentition at 1 year. Greater linear dimensional (LD) changes were observed at the midfacial aspect of the implant compared to the interproximal sites. The mean final IDES was 6.90 points, while patient-reported esthetic evaluation was 8.83 points. CONCLUSIONS: The present study demonstrated that vertical soft tissue augmentation with a submerged healing is an effective treatment approach for the treatment of challenging PSTDs with adjacent dentition exhibiting interproximal attachment loss. This technique can be effective in resolution of esthetic complications in most cases, providing a substantial gain in interproximal attachment levels at the adjacent dentition.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".