Clinical and psychological impact of lip repositioning surgery in the management of excessive gingival display
Bibliographic record
Abstract
Background: Excessive gingival display (EGD), also known as a gummy smile, is characterized by overexposure of the maxillary gingiva on smiling. EGD can cause embarrassment and reduce patient satisfaction. This study aimed to evaluate the clinical and psychological effects of lip repositioning surgery on the management of EGD. Methodology: This experimental study enrolled 14 patients with EGD who had undergone a modified lip repositioning technique, which comprised moving two strips of mucosa bilaterally to the maxillary labial frenum and repositioning the new mucosal margin coronally. The extent of gingival display (GD), lip mobility (LM), total lip length (TLL), lip length (LL), and internal lip length (ILL) was measured at baseline and 6 months postoperatively. The pre-operative psychological assessment was conducted using the social appearance anxiety scale (SAAS) scores, whereas the postoperative assessment was conducted using SAAS and visual analog scale (VAS) scores at 1 week, 3 months, and 6 months postoperatively. Results: Among the clinical parameters, TLL increased by 2.0 ± 1.038, LL increased by 2.28 ± 0.99, ILL reduced by 2.78 ± 1.36, LM reduced by 3.21 ± 1.12, and GD reduced by 3.14 ± 0.77 at 6 months postoperatively. Among the psychological parameters, SAAS reduced by 31.42 ± 1.907 from the baseline to 6 months, whereas the VAS score reduced to 3.14 ± 0.27 at 6 months postoperatively. Conclusion: A significant reduction in GD, which is largely dependent on strict case selection, pain, and social anxiety was observed in this study, indicating that lip repositioning surgery is effective in managing EGD.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".