Incidence and prevalence of peri‐implantitis and peri‐implant mucositis 17 to 23 (18.9) years postimplant placement
Bibliographic record
Abstract
PURPOSE: To evaluate the prevalence of peri-implantitis (PI) and peri-implant mucositis (PM) in a long-term follow-up with comparison among different PI and PM definitions, and to report on the incidence of PI. MATERIALS AND METHODS: In a retrospective clinical study five different PI and PM definitions were applied onto a population with 274 implants 17 to 23 years postimplant placement. Recommendations by the Eighth European Workshop on Periodontology (EWOP) were used as base reference. Clinical and radiological measurements were considered. Risk factors were evaluated in a regression analysis. RESULTS: After an average observation period of 18.9 years, 40.1% of the implants were diagnosed with PM and 15.0% with PI (Eighth EWOP). PI incidence reached 7.9% on implant level and 13.2% on patient level. Implants diagnosed with PI and progressive bone loss displayed exceptionally vertical bone defect configuration (BDC). Diabetes mellitus, smoking, regular maintenance, or a former periodontal infection did not show significant influence on the prevalence of peri-implant diseases. Patients with bruxism displayed significantly less PM and PI. CONCLUSIONS: Vertical BDC seems to correspond with active PI, wherefore we estimate such a defining factor of importance. Diagnosis of PM and evaluation of probing pocket depths might be only of descriptive interest as they could lead to false-positive results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".