Peri-implant disease: early diagnosis and non-surgical treatment—a narrative literature review
Bibliographic record
Abstract
Objective: The purpose of this narrative review is to help clinicians understand the main etiologic factor, commonly identified contributing factors, early diagnostic science, and non-surgical therapeutic modalities of peri-implant diseases. Background: Peri-implant diseases (i.e., peri-implant mucositis and peri-implantitis) are common biological complications of dental implant therapy, which may result in the failure of dental implants. Understanding the etiologic and contributing factors is the key to identify implants that are potentially at risk for peri-implant diseases. Furthermore, early diagnosis allows clinicians to execute initial non-surgical therapy in a timely manner, thus, potentially avoiding premature implant failure. Methods: Literature search was conducted using specific keywords related to diagnosis and non-surgical management of peri-implant diseases. Relevant literature between January 1st, 1993 and April 30th, 2021 were carefully selected and included in this narrative review. Conclusions: Patients with potential contributing factors for peri-implant diseases should be monitored carefully for early signs of peri-implant diseases. These factors include smoking habit, active or past history of periodontal diseases, unfavorable restorative conditions (i.e., excessive cements, excessive occlusion, limited access for plaque removal), lack of regular maintenance therapy, diabetes, and lack of keratinized tissue. Bleeding on probing appeared to be the most reliable early sign of peri-implant diseases. Initial non-surgical treatment should be delivered based on the principles of cause related therapy, specifically targeting the major etiologic factor for peri-implant diseases, dental plaque. During this phase, in addition to professional mechanical debridement, patient’s active participation in removing dental plaque through home oral care is indispensible to achieve successful outcome. Any contributing factor should be eliminated or minimized. In cases with advanced peri-implantitis, a surgical therapy is often necessary. After successfully resolving peri-implant diseases, a regular maintenance therapy should be provided which allows continuous monitoring of patients and their implants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".