Long‐term impact of patients' compliance to peri‐implant maintenance therapy on the incidence of peri‐implant diseases: An 11‐year prospective follow‐up clinical study
Bibliographic record
Abstract
OBJECTIVE: To prospectively evaluate the incidence of peri-implant diseases and the associated risk factors during 11 years of peri-implant maintenance therapy (PIMT). MATERIAL AND METHODS: A sample of 80 partially edentulous individuals rehabilitated with dental implants was invited to participate in a PIMT program (T1-prior to entering the PIMT program). After 11 years, 51 individuals remained regularly or irregularly adherent to PIMT (T2-last recall after 11 years) and were classified as regular (RC; n = 27) or irregular (IC; n = 24) compliers. Data of interest were analyzed using univariate and multivariate logistic regression analyses. RESULTS: The incidence of peri-implant mucositis and peri-implantitis observed at T2 in the IC group (70.8% and 37.5%, respectively) were significantly higher than those observed in the RC group (37.0% and 11.1%, respectively). The incidence of peri-implant diseases was mostly attributable to potentially modifiable risk factors, as such: RC group-PM (p = 0.013); IC group-high plaque index (p < 0.001), irregular compliance (p < 0.001), the presence of PM (p = 0.015) and periodontitis (p < 0.039). CONCLUSION: Regular compliance during PIMT had a strong effect in minimizing the incidence of peri-implant diseases. Increasing regular dental visits and improving oral hygiene would provide benefits for preventing peri-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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".