Prevalence of Peri‐Implant Diseases in Computer‐Guided Implant Sites: A Cross‐Sectional Study
Bibliographic record
Abstract
OBJECTIVE: The present cross-sectional study aimed to determine the prevalence and the risk indicators associated with peri-implant diseases (PIDs) in patients who were subjected to computer-guided implant surgery. METHODS: Patients that were treated and screened during regular maintenance visits at one university center were enrolled in this cross-sectional study. Implants were diagnosed into the categories of peri-implant health, peri-implant mucositis, or peri-implantitis according to the 2017 World Workshop established case definitions. Bivariate and multivariable analyzes were conducted to identify local parameters and patient characteristics as associated risk indicators with PIDs, bleeding on probing (BOP) and marginal bone level (MBL) change. RESULTS: A total of 115 patients with 417 implants were evaluated during a regular maintenance visit at one university center. Peri-implant mucositis and peri-implantitis prevalence in digitally-guided implant sites were 67.8% and 9.6% at the patient level, respectively. Former and active smokers, active or a history of periodontitis, implant loading time, plaque index (PI), and absence of soft tissue graft were significantly associated with peri-implantitis. Bruxism, gastrointestinal (GI) disorders, and type of oral hygiene aid (OHI) displayed a significant association with peri-implant MBL changes. CONCLUSION: The prevalence of peri-implant diseases in digitally-guided implant sites was comparable for mucositis and appeared lower for peri-implantitis when compared to previous outcomes with nondigital guided implant placement. Notably, patient-related factors and local clinical characteristics such as smoking, periodontitis, higher PI scores, and implant loading time were significantly associated with the occurrence of PIDs, while soft tissue grafting had a protective effect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".