Soft tissue features of peri‐implant diseases and related treatment
Bibliographic record
Abstract
BACKGROUND: The need for soft tissue grafting at implant sites for preventing and treating peri-implant diseases is a currently investigated and debated topic. PURPOSE: The aim of this manuscript is to explore the inflammatory mechanisms at the peri-implant soft tissue compartment, to distinguish the structural components of the peri-implant soft tissue phenotype and their role on peri-implant health, and to appraise the clinical indications and expected outcomes of soft tissue augmentation procedures at peri-implant diseased sites. MATERIALS AND METHODS: This narrative review depicts the inflammatory biomarkers and mediators in the peri-implant crevicular fluid utilized to diagnose peri-implant disease and that have been shown to be associated with peri-implant soft tissue phenotype modification and disease resolution. The impact of the peri-implant soft tissue phenotype, involving keratinized mucosa (KM) width, attached mucosa (AM), mucosal thickness (MT), and supracrestal tissue height (STH), on peri-implant health, esthetic, patient's comfort and disease prevention are discussed. The manuscript also illustrates the use of ultrasonography for the detection of peri-implant health/disease and the evaluation of the treatment outcomes following surgical therapies. RESULTS: Current evidence indicates that soft tissue phenotype modification at implant sites with inadequate KM width, AM and MT can be beneficial for promoting peri-implant health and improving patient's comfort and hygiene procedures. Treatment approaches and outcomes from the available literature on soft tissue phenotype modification in combination with conventional techniques at sites with peri-implant mucositis or peri-implantitis are presented and discussed in detail. CONCLUSIONS: Soft tissue grafting can be beneficial in preventing and treating peri-implant diseases. Clinical recommendations based on the disease, soft tissue phenotype characteristics and bone defect morphology are provided for a comprehensive hard- and soft-tissue-oriented treatment of peri-implant disease.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".