Prevalence of peri‐implantitis after alveolar ridge preservation at periodontitis and nonperiodontitis extraction sites: A retrospective cohort study
Bibliographic record
Abstract
INTRODUCTION: Periodontitis is the main indication for dental extraction and often leads to peri-implantitis (PI). Alveolar ridge preservation (ARP) is an effective means of preserving ridge dimensions after extraction. However, whether PI prevalence is lower after ARP for extraction after periodontitis remains unclear. This study investigated PI after ARP in patients with periodontitis. MATERIALS AND METHODS: This study explored the 138 dental implants of 113 patients. The reasons for extraction were categorized as periodontitis or nonperiodontitis. All implants were placed at sites treated using ARP. PI was diagnosed on the basis of radiographic bone loss of ≥3 mm, as determined through comparison of standardized bitewing radiographs obtained immediately after insertion with those obtained after at least 6 months. Chi-square and two-sample t testing and generalized estimating equations (GEE) logistic regression model were employed to identify risk factors for PI. Statistical significance was indicated by p < 0.05. RESULTS: The overall PI prevalence was 24.6% (n = 34). The GEE univariate logistic regression demonstrated that implant sites and implant types were significantly associated with PI (premolar vs. molar: crude odds ratios [OR] = 5.27, 95% confidence intervals [CI] = 2.15-12.87, p = 0.0003; bone level vs. tissue level: crude OR = 5.08, 95% CI = 2.10-12.24; p = 0.003, respectively). After adjustment for confounding factors, the risks of PI were significantly associated with implant sites (premolar vs. molar: adjusted OR [AOR] = 4.62, 95% CI = 1.74-12.24; p = 0.002) and implant types (bone level vs. tissue level: AOR = 6.46, 95% CI = 1.67-25.02; p = 0.007). The reason for dental extraction-that is, periodontitis or nonperiodontitis-was not significantly associated with PI. CONCLUSION: ARP reduces the incidence of periodontitis-related PI at extraction sites. To address the limitations of our study, consistent and prospective randomized controlled trials are warranted.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".