Influence of vertical soft tissue thickness on occurrence of peri‐implantitis in patients with periodontitis: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: The influence of vertical soft tissue thickness on health of peri-implant tissue has been addressed in few studies; thus, further research is needed. PURPOSE: To evaluate the influence of vertical soft tissue thickness on the risk of peri-implantitis and peri-implant condition in patients with a history of periodontitis. DESIGN/METHODS: A total of 92 patients with a history of periodontitis were included in this study. During the implant placement, the distance from palatal soft tissue edge to the alveolar crest, which was defined as vertical soft tissue thickness, was measured. The characteristics of patients and implants were recorded at baseline (T1). After more than 2 years follow-up period, 65 patients with 159 implants finally came back for T2 evaluation, and parameters of peri-implant tissue were recorded. The associations of vertical soft tissue thickness with peri-implant parameter and occurrence of peri-implantitis were analyzed using the generalized estimating equation accompanying linear regression and logistic regression. In addition to conventional multivariate analysis, a propensity score for adjustment was used to reduce confounding bias. RESULTS: At follow-up examination (T2), survival rate at implant-level was 98.8%. The peri-implant parameters, including peri-implant probing depth and marginal bone loss, increased significantly with the increasing of soft tissue thickness (P < 0.05) after adjusting for a propensity score. In multivariate analysis adjusted for different confounding factors and propensity score, the odd ratios were all approximately 2.5, which meant that the risk of peri-implantitis increased 1.5 times for 1 mm increase of soft tissue thickness. CONCLUSIONS: The excessive vertical soft tissue thickness around implants in patients with history of periodontitis has an adverse influence on health of the peri-implant tissue. This observation raises an important question on the association between vertical soft tissue thickness during implant surgery and history of periodontitis. Effective approaches to prevent the adverse effect of excessive soft tissue thickness on peri-implant tissue are necessary to be further investigated.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".