Network Meta‐Analysis for Evaluating Interventions in Implant Dentistry: The Case of Peri‐Implantitis Treatment
Bibliographic record
Abstract
BACKGROUND/AIM: Evidence from head-to-head comparison trials on peri-implantitis treatment is limited, and it is therefore impossible to conduct a direct meta-analysis. We propose an alternative statistical method, network meta-analysis, for evidence synthesis, which enables to compare the results of multiple treatments. METHODS: We searched, in triplicate, for randomized controlled trials (RCTs) and controlled trials in the PubMed, Cochrane Central Register of Controlled Trials, Clinicaltrials.gov, and Latin American and Caribbean Health Sciences Literature databases up to and including August 2010. We also conducted a manual search of the reference lists regarding published systematic reviews and searched for gray literature in OpenSIGLE. We assessed changes in clinical attachment level (CAL) and pocket probing depth (PPD) after nonsurgical and surgical treatments of peri-implantitis. The risk of bias of selected studies was determined by the use of specific criteria, and it was performed in triplicate and independently. We used multilevel mixed modeling to perform the network meta-analysis and Markov Chain Monte Carlo simulation to obtain confidence intervals for the fixed and random effects. RESULTS: Eleven studies were included in the review. All RCTs are at unclear or high risk of bias. Surgical therapy in conjunction with bone grafts and non-resorbable membranes achieved 3.52 mm greater PPD reduction than nonsurgical therapy alone, 95% high-probability density (HPD) intervals: -0.19, 6.81. Surgical treatment in conjunction with bone grafts and resorbable membranes achieved 2.80 mm greater CAL gain than nonsurgical therapy alone, 95% HPD intervals: -0.18, 5.59. CONCLUSION: Surgical procedures in peri-implantitis treatment achieve more PPD reduction and CAL gain than nonsurgical approaches. Nevertheless, these results should be interpreted with caution because of the limited number of studies included and their low methodological quality. Network meta-analysis is a useful statistical methodology for evidence synthesis and to summarize the strength and limitation in the current evidence.
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 | 0.146 | 0.309 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".