Surgical interventions for the treatment of peri‐implantitis
Bibliographic record
Abstract
BACKGROUND: Peri-implantitis constitutes one of the most frequent late dental implant complications. The disease is initiated by bacterial infection; therefore, anti-infective peri-implantitis treatment strategies are required to arrest the progressive marginal bone loss and maintain the affected implant in function. Although nonsurgical treatment strategies appear to be of limited predictability, treatment outcomes have been frequently improved following surgical interventions. PURPOSE: The present narrative review describes various surgical peri-implantitis treatment modalities, with respect to their indications, performance, and effectiveness. MATERIALS AND METHODS: The present narrative review considered the most relevant studies in the field published in the English language. RESULTS: Surgical peri-implantitis treatment approaches can be categorized as nonreconstrucive therapy, reconstructive therapy, and combined therapy (ie, reconstructive and resective therapy). In addition to disease resolution, reconstructive approaches also seek to regenerate the bone defect and achieve reosseointegration. CONCLUSIONS: The severity of the disease, the regenerative potential of the defetc and esthetic demands of the patient are the factors determining the surgical peri-implantitis treatment modality.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".