Management of Physiological Gingival Pigmentation Using Two Treatment Modalities: Clinical and Patient-Reported Outcomes
Bibliographic record
Abstract
Objectives: Physiological gingival pigmentation is considered aesthetically unappealing, and individuals diagnosed with it often want to reduce or remove the pigmentation. There are various techniques for depigmentation, with laser treatment considered the simplest, most reliable and most cost-effective option. This study assessed two different treatments for physiological gingival pigmentation. Materials and methods: A total of 12 patients who had gingival pigmentation were randomly divided into a control group (treated with a bur) and a test group (treated with a laser). The same experienced periodontist performed all the procedures. Patient-reported outcomes of the treatments were obtained through a quality of life questionnaire adapted from Melzack's McGill pain questionnaire, which patients completed one day, one week and four weeks after therapy. Additionally, 15 experienced dentists assessed clinical photographs using a Likert scale to compare the clinical outcomes before and one month after the intervention. Results: Patients were similarly satisfied with both laser and bur depigmentation treatments (54.17%, mean = 2.71, SD = 0.624; 50.0%, mean = 2.50, SD = 1.034, respectively). The patients' satisfaction results were non-significant (P > 0.05), except in terms of noticing an aesthetic change, which was significant (P < 0.05) in favor of the laser approach. The satisfaction rate was higher for laser treatment (84.44%, mean = 4.21) compared to bur depigmentation (78.67%, mean = 3.93). The results were statistically non-significant (P > 0.05). One month after treatment, four patients from the control group reported that the treatment met their expectations, while one participant said it exceeded expectations. Conclusion: Within the limitations of this study, the findings suggested that laser treatment was slightly superior to bur treatment for managing gingival pigmentation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".