The Effects of Photodynamic Therapy as a Treatment for Peri-implantitis Patients: A Literature Review
Bibliographic record
Abstract
Introduction: Over the past decade, dental implant procedures have been studied to have high success rates. However, instances of complications have been observed among a large scope of patients, leading to diseases such as peri-implantitis (PI). Developing modern day treatments for PI, such as photodynamic therapy (PDT), is significant in discovering new maintenance strategies. This review explores the effects of PDT in the treatment of PI. Methods: This review covers a broad range of studies published between 1995 to 2024. The studies highlight the results of PDT treatment as an adjuvant to mechanical/open flap debridement in the treatment of PI. The methodologies used during the analysis involved systematic literature review and meta-analysis. All literature reviewed is sourced from Google Scholar and online databases such as PubMed, ScienceDirect, and Scopus. Results: Upon PDT treatment, as an adjunct to mechanical/open flap debridement, a significant decrease within BOP (bleeding on probing) and plaque index was distinguished. However, there was no significant difference between results in BOP and plaque index for the PDT groups in comparison to the control groups (debridement alone). For pocket probing depth (PPD), a significant difference is highlighted between the PDT and control groups at 3 and 6 months, as PPD was observed to be lower. Different adjuvant therapies compared to PDT posed different challenges ranging from lower effectiveness, allergen threats, and the development of bacterial resistance. Discussion: There were not any prominent disadvantages observed in receiving PDT, however, factors such as time and number of sessions need to be weighed in to determine if small improvements are ultimately worth the commitment to the therapy. In terms of comparison to different adjuvant therapies, the choice of treatment varies based on the individual's allergies and the severity of the condition for PI. Conclusion: Applying PDT as an adjuvant to traditional debridement treatments does not seem to contribute an extensive improvement for treating PI cases. If a patient is looking to find treatments that provide smaller improvements for PI, PDT may serve as a safe treatment. Further development in PDT needs to be conducted to achieve more optimal results in treating PI.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 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) | 0.004 | 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".