Comparative Study Between the Effects of Photodynamic Therapy and Conventional Therapy on Microbial Reduction in Ligature‐Induced Peri‐Implantitis in Dogs
Bibliographic record
Abstract
BACKGROUND: Progressive peri-implant bone losses, which are accompanied by inflammatory lesions in the soft tissues, are referred to as peri-implantitis. The aim of this study was to compare the effects of photodynamic therapy (PDT) and conventional technique on microbial reduction in ligature-induced peri-implantitis in dogs. METHODS: Eighteen third premolars from nine Labrador retriever dogs were extracted and the implants were submerged. After osseointegration, peri-implantitis was induced. After 4 months, ligature was removed and natural bacterial plaque was allowed to form for another 4 months. The animals were then randomly divided into two groups. In the conventional group, they were treated using mucoperiosteal flaps for scaling the implant surface and chlorexidine (conventional) irrigation. In the PDT group, only mucoperiosteal scaling was carried out before photodynamic therapy. Inside the peri-implant pocket, a paste-based azulene photosensitizer was placed and then a GaAlAs low-power laser (lambda=660 nm, P=40 mW, E=7.2 J for 3 minutes) was used. Microbiological samples were obtained before and immediately after treatment. Before treatment, one implant was removed and analyzed by scanning electron microscopy to validate the contamination. RESULTS: The results of this study showed that Prevotella sp., Fusobacterium sp., and S. Beta-haemolyticus were significantly reduced for both groups. After treatment, no significant differences were observed between the groups. CONCLUSION: These findings suggest that photodynamic therapy is a non-invasive method that could be used to reduce microorganisms in peri-implantitis.
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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 | 0.000 |
| 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) | 0.001 | 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".