Clinical Effectiveness of Gingival Depigmentation Using Conventional Surgical Scrapping and Diode Laser Technique: A Quasi Experimental Study
Bibliographic record
Abstract
Excessive gingival pigmentation is a major aesthetic concern in modern society, though it is not a medical problem they consider it as a negative attribute. Patients with gingival hyperpigmentation usually complain and request cosmetic therapy, particularly if the pigmentation is visible during speaking and smiling. Various depigmentation methods, including burr abrasion, cryosurgery, electro-surgery, split thickness flap excision and surgical scraping techniques have been used with varying degrees of success. Recently, lasers have been used to ablate cells containing and producing the melanin pigment. The present study was undertaken to compare the clinical effectiveness and patient comfort of surgical scrapping and diode laser technique used for gingival depigmentation for a follow up period of 6 months.20 subjects participated in this split mouth study. The clinical evaluation parameters included Extent and Intensity of gingival hyperpigmentation, post-operative gingival bleeding and pain. On follow up examination at 6th month there was no statistical difference in repigmentation extent and intensity between diode laser and surgical scraping techniques. The mean pain scores for treated sites with diode laser were significantly lower than surgical scrapping technique at 24 hours (t-value=2.430, p-value=0.02). The postoperative gingival bleeding at end of procedure was significantly lower with diode laser than surgical scrapping technique (p-value=<0.0001). There was no statistical difference in postoperative re-pigmentation and clinical efficacy among the subjects between surgical scraping and diode laser technique at 6th month follow up. Diode laser technique provides better haemostasis and good visibility at the surgical site. The post-operative patient comfort is better at the surgical sites treated with diode laser than surgical scrapping method. Hence, both the techniques are used for depigmentation procedures depending on the severity and gingival biotype and patient acceptance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".