Physiopathology of peri‐implant diseases
Bibliographic record
Abstract
BACKGROUND: Peri-implant health is characterized by the absence of clinical signs of soft tissue inflammation. Peri-implant diseases are initiated by the presence of bacterial biofilms and share a similar etiology as that involved in the onset of periodontal diseases. PURPOSE: To summarize available evidence on the physiopathology of peri-implant diseases with emphasis on similarities and differences with periodontal diseases. MATERIALS AND METHODS: Evidence on the biologic mechanisms involved in the pathogenesis of peri-implant mucositis and peri-implantitis were explored in the recent scientific literature. RESULTS: Findings of studies in animals and in humans indicate that experimental peri-implant mucositis leads to a larger inflammatory connective tissue infiltrate and to a higher frequency of bleeding sites around implants compared with teeth. Tissue destruction at experimental peri-implantitis sites is more pronounced compared with that at experimental periodontitis sites. Although human periodontitis and peri-implantitis lesions share similarities with respect to etiology and clinical features, they represent distinct entities from a physiopathologic point of view. CONCLUSIONS: Diagnosis of peri-implant health requires a clinical examination to confirm absence of peri-implant soft tissue inflammation. In order to make a correct diagnosis and select the appropriate therapeutic steps to manage peri-implant diseases, knowledge of their pathogenetic mechanisms is required.
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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 | 0.001 |
| 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) | 0.003 | 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".