Unraveling the effectiveness of antibiotics for peri‐implantitis treatment: A scoping review
Bibliographic record
Abstract
BACKGROUND: Antibiotics are the most effective adjuncts in the treatment of periodontitis. However, the benefits of these agents in treating peri-implantitis are still debatable and demand further analysis. PURPOSE: The aim of this review was to critically appraise the literature on the use of antibiotics to treat peri-implantitis, with the ultimate goal of supporting evidence-based clinical recommendations, defining gaps in knowledge and guiding future studies on this topic. METHODS: A systematized literature search was conducted in MEDLINE/PubMed and Cochrane Library databases for randomized clinical trials (RCTs) on patients with peri-implantitis treated by mechanical debridement-only or with adjunctive use of local or systemic antibiotics. Clinical and microbiological data were extracted from the RCTs included. The findings were critically reviewed, interpreted, and discussed. An overview of antibiotic-loaded dental implant materials in peri-implantitis treatment was also provided. RESULTS: Twelve RCTs testing local/systemic antibiotics were included. Although not always statistically significant, all antibiotic-treated groups had greater reductions in mean PD than those treated by mechanical debridement-only. The only clinically relevant antibiotic protocol supported by one RCT with low risk of bias and long-lasting benefits was systemic metronidazole (MTZ). Studies using ultrasonic debridement reported better outcomes. No RCTs to date have tested MTZ-only or with amoxicillin (AMX) as adjuncts to open-flap implant debridement. In vitro/animal studies suggested that biomaterials with antimicrobial properties are promising to treat peri-implantitis. CONCLUSION: There are insufficient data to support a particular evidence-based antibiotic protocol to treat peri-implantitis using surgical or nonsurgical therapy, but some conclusions may be drawn. Systemic MTZ adjunct to ultrasonic debridement is an effective protocol to improve the outcomes of nonsurgical treatment. Future studies should assess the clinical and microbiological effects of MTZ and MTZ + AMX as adjuncts to optimal nonsurgical implant decontamination protocols or open-flap debridement. In addition, new locally delivered drugs and antibiotic-loaded surfaces should be assessed by RCTs.
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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 | 0.018 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".