A Systematic Review and Meta-Analysis Comparing Surgical and Nonsurgical Treatments for Excessive Gingival Display
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it