Effects of Photodynamic Therapy on Clinical and Gingival Crevicular Fluid Inflammatory Biomarkers in Chronic Periodontitis: A Split‐Mouth Randomized Clinical Trial
Bibliographic record
Abstract
BACKGROUND: There are limited clinical experiments addressing the effects of photodynamic therapy (PDT) as an adjunct to conventional scaling and root planing (SRP) on clinical and biologic features of periodontitis. This trial compares the clinical parameters and cytokine profiles in gingival crevicular fluid of patients with moderate-to-severe chronic periodontitis (CP) who have been treated using SRP alone or SRP + PDT. METHODS: Twenty-two patients with two contralateral teeth affected with moderate-to-severe CP were selected. After SRP, the participants' teeth were randomized to receive either no further treatment or a single application of PDT using a 638-nm laser and toluidine blue. Although the change in probing depth was the primary outcome, bleeding on probing, clinical attachment level, gingival recession, interleukin-1β, tumor necrosis factor (TNF)-α, and matrix metalloproteinase 8 and 9 were also evaluated at baseline and 3 months postintervention. An oral rinse assay was also performed to determine the total levels of oral polymorphonuclear cells (PMNs) before and 3 months after the treatments. RESULTS: Within each group, significant improvements (P <0.001) were found for all variables in 3-month follow-up compared with baseline. Only TNF-α was significantly improved in the PDT + SRP versus SRP group. Total levels of PMNs were reduced for all patients compared with baseline levels (P <0.001). CONCLUSION: In patients with CP, a single application of PDT (using a 638-nm laser and toluidine blue) did not provide any additional benefit to SRP in terms of clinical parameters or inflammatory markers 3 months following the intervention.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".