Health, Maintenance, and Recovery of Soft Tissues around Implants
Bibliographic record
Abstract
BACKGROUND: The health of peri-implant soft tissues is one of the most important aspects of osseointegration necessary for the long-term survival of dental implants. PURPOSE: To review the process of soft tissue healing around osseointegrated implants and discuss the maintenance requirements as well as the possible short-comings of peri-implant soft tissue integration. MATERIALS AND METHODS: Literature search on the process involved in osseointegration, soft tissue healing and currently available treatment modalities was performed and a brief description of each process was provided. RESULTS: The peri-implant interface has been shown to be less effective than natural teeth in resisting bacterial invasion because gingival fiber alignment and reduced vascular supply make it more vulnerable to subsequent peri-implant disease and future bone loss around implants. And we summarized common procedures which have been shown to be effective in preventing peri-implantitis disease progression as well as clinical techniques utilized to regenerate soft tissues with bone loss in advanced cases of peri-implantitis. CONCLUSION: Due to the difference between peri-implant interface and natural teeth, clinicians and patients should pay more attention in the maintenance and recovery of soft tissues around implants.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".