Bibliographic record
Abstract
Objective: The objective of this study was to evaluate the efficacy of the novel approach of non- surgical therapy using photodynamic laser therapy for treatment of periodontal lesions. Material and Methods: This study included 60 patients with at least one periodontal pocket ≤4 mm in all four quadrants of the mouth, without systemic disease and without prior 6-month use of antibiotics. A split-mouth design was used to compare treatment options for four quadrants: Scaling and Root Planning (SRP), SRP + Photodynamic Therapy (PDT), SRP + Low-Level-Laser-Therapy (LLLT) and basic therapy. Baseline, 3 and 6 months’ post-treatment periodontal examinations included Pocket Probing Depth (PPD), Clinical Attachment Level (CAL), Gingival Recession (GR), Gingival Index (GI), and Bleeding-on-Probing (BOP). Microbiological sampling from the periodontal pockets for anaerobic periopathogens using commercially available kits took place simultaneously with periodontal examinations. An examiner blinded to the treatment procedure used sterile paper point #50 for sampling from each quadrant and cultivated the material from the paper in a Schaedler terrain. The culture was put in a hermetically sealed bag with anaerobic-generator sacculus, incubated for 48 h and read with commercially available anaerobic ID cards in the Vitek-2? machine. Results: The results showed a significant reduction of PPD, CAL, GI and BOP for the quadrants treated with SRP + PDT, while SRP + LLLT and SRP alone showed similar results. When comparing CAL between the working groups, the SRP + PDT group showed significantly higher CAL gain than the other two study groups after 3 and 6 months (p < 0.01 and p < 0.001, respectively). The analysis of variance between the groups regarding the periodontal parameters, except for the GR, showed better results for the SPR + PDT group, but without any difference in microbiological findings. Conclusion: Photo-dynamic therapy, as an adjunct to the non-surgical method, may enhance mechanical debridement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.927 | 0.918 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".