A Systematic Review and Meta-Analysis Comparing Surgical and Nonsurgical Treatments for Excessive Gingival Display
Bibliographic record
Abstract
Excessive gingival display (EGD) is defined as more than 2 mm of gingiva display above the maxillary incisors at maximum smile. Various skeletal, dental, and soft tissue etiological factors for EGD have been suggested. This study assessed the effectiveness and stability of surgical (SX) and nonsurgical (NSX) interventions for correction of EGD through a systematic review and meta-analysis following PRISMA 2020 guidelines. An electronic search of Ovid MEDLINE, EMBASE, CENTRAL, Scopus, Web of Science, and LILACS was conducted (2010-2023). Results were expressed as mean change in gingival display using the random-effects model at 1, 3, 6, and 12-month follow-up. At 1 month, SX and NSX treatments yielded a comparable mean reduction of 3.50 mm (2.13-4.86) and 3.43 mm (2.67-4.19) in gingival display, respectively. However, by 6 months, NSX treatments showed a reduction of 0.51 mm compared to 2.86 mm with SX treatments. SX outcomes remained stable past 6 months, while NSX outcomes partially relapsed at 6 months and returned to baseline levels at 12 months. Notably, NSX treatments were more effective in cases with mild initial EGD, while SX treatments showed a better outcome in severe cases. To draw more robust conclusions regarding the treatment outcomes, future primary studies of greater rigor are required.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".