Probing Depth Reduction Following Peri‐Implantitis Treatment: A Systematic Review and Component Network Meta‐Analysis
Bibliographic record
Abstract
OBJECTIVE: In this update of a previous systematic review, we compared the effects of surgical and non-surgical treatments for peri-implantitis through the component network meta-analysis (CNMA) with probing depth (PD) reduction as the outcome. MATERIALS AND METHODS: Literature search was conducted in PubMed, Cochrane Central Register of Controlled Trials, and Embase databases from August 2010 to June 2023. Randomized controlled trials (RCTs), comparing non-surgical or surgical treatments for peri-implantitis with 6-12 months of follow-up and reported changes in PD, were included. Treatment effects were assessed using a CNMA model based on additivity assumption. We calculated the intraclass correlation coefficient (ICC) to adjust the standard errors for multiple implants within the same patient. RESULTS: Our systematic review identified 44 RCTs, which included 46 treatment regimens consisting of 15 components. These RCTs formed a disconnected network consisting of 11 subnetworks. Surgical treatments with bone grafts and membranes generally attained greater PD reduction than non-surgical treatments, although bone grafts and membranes as components provided moderate benefits. The effect size of antibiotics is greater in non-surgical than surgical treatments, while there is considerable uncertainty regarding the effect size of implantoplasty. Additionally, the effectiveness of components varied between surgical and non-surgical treatments. CONCLUSION: Current evidence does not yield sufficiently robust estimates for identifying optimal surgical and non-surgical treatment regimens for peri-implantitis, so the findings of our study should be interpreted cautiously. A coordinated strategy is required for designing future trials to fill the gaps in our current knowledge and develop more reliable recommendations.
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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 | 0.024 | 0.064 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".