Efficacy of supportive peri-implant therapy in the management of peri-implant mucositis and peri-implantitis
Bibliographic record
Abstract
BACKGROUND: Peri-implant mucositis and peri-implantitis are increasingly prevalent, necessitating evidence-based maintenance strategies. Supportive peri-implant therapy (SPiT) protocols are proposed to limit disease progression and enhance clinical outcomes, yet their effectiveness remains variably reported. The authors aimed to systematically evaluate the effectiveness of SPiT protocols in preventing or reducing the progression of peri-implant diseases, with a primary focus on radiographic bone loss, clinical parameters, and patient-reported outcomes. TYPES OF STUDIES REVIEWED: A comprehensive systematic search was conducted in MEDLINE, Embase, Cochrane Library, and Web of Science through April 30, 2025. Eligible studies included randomized controlled trials, cohort studies, and case-control studies assessing professionally delivered SPiT. Risk of bias was assessed using the Cochrane of risk of bias tool for randomized trials ROB 2.0 and the Newcastle-Ottawa Scale for observational studies. Due to considerable clinical and methodological heterogeneity, results were synthesized narratively. RESULTS: Twenty-five studies met inclusion criteria: 9 randomized controlled trials, 13 cohort studies, and 3 case-control studies. SPiT, particularly with individualized, risk-based recall intervals, consistently improved clinical outcomes, such as probing depth and bleeding on probing compared with standard or no maintenance. However, substantial variability existed regarding adjunctive therapies, patient compliance, and reporting of systemic comorbidities. High clinical and methodological heterogeneity among studies, inconsistent documentation of systemic conditions, and variability in adjunctive therapies limited definitive conclusions and generalizability. CONCLUSIONS AND PRACTICAL IMPLICATIONS: Individualized risk-based SPiT protocols substantially enhance clinical outcomes and implant longevity compared with standard fixed-interval maintenance or no supportive therapy. Future research should standardize reporting, better account for patient-specific risk factors, and clarify the long-term effectiveness of adjunctive therapies. Risk-based SPiT protocols should be integrated into routine implant maintenance to improve clinical outcomes and reduce disease progression. Clinicians are encouraged to tailor recall intervals and adjunctive measures on the basis of patient-specific risk factors, including history of periodontitis, smoking status, and systemic health. Adopting individualized SPiT protocols can enhance implant longevity and minimize complications.
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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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".