The Effect of Photobiomodulation Therapy on Inflammation Following Dental Prophylaxis
Bibliographic record
Abstract
To evaluate the effectiveness of photobiomodulation therapy (PBMT) in reducing gingivitis following professional dental prophylaxis in dogs in a prospective, randomly controlled, double-blind study. The study included 47 dogs (male n = 23, female n = 24), with a mean age of 6.5 ± 2.6 years and a bodyweight of 28.9 ± 5.3 kg. Breeds included: Belgian Malinois Shepherd Dogs (n = 23), German Shepherd Dogs (n = 14), Labrador Retriever (n = 4), and Dutch Shepherd Dogs (n = 3). The left side of the mouth was treated by performing dental prophylaxis (control group). The right side received dental prophylaxis and a single session of PBMT using a Class IV therapeutic laser (treatment group). Each side was graded individually for periodontal disease (PD), gingivitis, and calculus. Follow-up observations were conducted on days 1, 3, 8, and 15 post-treatment. Results were compared using a Mann-Whitney Test. Multiple regression was run to predict PD, gingivitis, and calculus scores using age, sex, and breed. A p < .05 was considered significant. The PBMT group (PG) showed significantly lower gingivitis scores from day 1 to 15 post-treatment. PD had an increasing prevalence with age. Age and breed contributed to the prediction of PD, gingivitis, and calculus grading. This study showed that PBMT significantly reduced the grade of gingivitis following dental prophylaxis, and may be a useful adjuvant procedure following dental prophylaxis.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".