The gain in keratinized gingiva using apically positioned flap during implant placement with and without ridge augmentation: A comparative prospective study
Bibliographic record
Abstract
OBJECTIVES: To evaluate whether ridge augmentation (RA) affects the gain in keratinized gingiva (KG) in implant surgery using a full-thickness apically positioned flap (fAPF). MATERIAL AND METHODS: We conducted a prospective study from April 2017 to April 2019 recording patient- and implant-related factors. The subjects underwent fAPF during implant placement and were divided two groups: Group A, one-stage surgical protocol without RA; Group B, two-stage surgical protocol with RA. The initial width of KG and the width of KG at 1 week, 3 weeks, 3 months, and 6 months after surgery and baseline were measured using a paper ruler. Multivariable generalized estimating equation (GEE) models were estimated to evaluate RA effects on the gain in KG, the shrinkage amount of KG, and shrinkage ratio of KG after fAPF. RESULTS: Seventy-nine participants with 203 implants were included. The baseline values of KG were 1.68 mm in Group A and 0.82 mm in Group B (p < 0.001). The results of the multivariable GEE demonstrated that the gain in KG, the shrinkage amount of KG, and the shrinkage ratio of KG showed no significant difference in groups (p > 0.05). The gain in KG was 1.92 ± 1.67 mm in Group A, 1.48 ± 1.36 mm in Group B. The total shrinkage amount and the shrinkage ratio of KG were 1.87 mm and 42.43%, respectively. CONCLUSIONS: A fAPF is a reliable technique that enables significant increase in KG regardless of RA in implant surgery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".