Regenerative Treatment of Peri-Implantitis Using Bone Substitutes and Membrane: A Systematic Review
Bibliographic record
Abstract
PURPOSE: This systematic review aimed to assess the available literature for regenerative treatment of peri-implantitis using bone graft substitutes and membranes. METHODS: A search in electronic databases was conducted to assess all types of clinical studies treating bone defects derived from peri-implantitis using guided bone regeneration (GBR) techniques. RESULTS: During the first screening, 399 titles were identified. Finally, 17 articles reporting on 173 implants were included. The articles mainly focused on radiographic bone fill of the defect. Qualitative measures of "bone fill" were reported: 10.4% of the implants showed complete "bone fill," whereas 85.5% revealed incomplete defect closure. No bone fill was shown in 4.0%. Little information (in 53.2%) was provided regarding the probing depth before or after treatment. Data concerning the inflammatory status of soft tissues were also scarce and only reported in three studies. A large heterogeneity concerning disinfection protocols and regenerative materials used was found. The high percentage of low-quality studies rendered a meta-analysis impossible. CONCLUSION: Complete fill of the bony defect using GBR seems not to be a predictable outcome. The mucosal health status is left unconsidered in most studies. Well-controlled trials are needed to determine predictable treatment protocols for the successful regenerative treatment of peri-implantitis using GBR technique.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".