Prevention and management of peri‐implant disease
Bibliographic record
Abstract
BACKGROUND: As more patients choose dental implants as their primary treatment option to restore edentulous ridges or to replace compromised dentition, preventive strategies for peri-implant diseases and complications have become an important topic. PURPOSE: The aim of the review article is to summarize the current available evidence on the potential risk factors/indicators for peri-implant disease development and then focus on the preventive strategies for peri-implant diseases and conditions. MATERIALS AND METHODS: After reviewing the diagnostic criteria and the etiology of peri-implant diseases and conditions, evidence on the possible associated risk factors/indicators for peri-implant diseases were searched and identified. Recent studies were also surveyed to explore the preventive measures for peri-implant diseases. RESULTS: The possible associated risk factors of peri-implant diseases can be divided into the following categories: patient-specific factors, implant-specific factors, and long-term factors. Patient-specific factors such as history of periodontitis and smoking have been conclusively associated with peri-implant diseases, whereas findings on others, such as diabetes and genetic factors, remain inconclusive. It has been suggested that both implant-specific factors, such as implant position, soft tissue characteristics, and the type of connection used, and long-term factors, such as poor plaque control and a lack of maintenance program, have a strong impact on maintaining the health of a dental implant. Assessment tool for evaluating the risk factors can be a potential preventive measure for peri-implant disease prediction, and it is needed to be properly validated. CONCLUSION: Proper maintenance program for early intervention to control peri-implant diseases at the initial stage and pretreatment assessment of the potential risk factors is the best strategy to prevent implant diseases.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".