Surgical procedures for soft tissue augmentation at implant sites. A systematic review and meta‐analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Different procedures were proposed to augment soft tissue around dental implants. OBJECTIVE: Aims of this Systematic Review (SR) were to evaluate (a) clinical benefit of soft tissue augmentation at implant sites (b) which is the best surgical procedure to augment soft tissue. MATERIALS AND METHODS: Manual/electronic searches were performed to identify randomized controlled trials (RCTs). Change in keratinized tissue thickness (STT) and height (KT) were primary outcomes. Random effects meta-analyses were performed where suitable and expressed as weighted mean differences (MD) with their associated 95% confidence intervals (CI). RESULTS: Fourteen RCTs accounting for 475 patients and 538 implants were included. Only five studies were judged at low risk of bias. In the single studies, soft augmentation lead to higher STT and KT compared to no augmentation. Considering primary outcomes, connective tissue graft (CTG) was more effective than xenogeneic collagen matrix (XCM) to improve STT (MD: -0.30 mm; 95% CI -0.43; -0.17; P < .00001) in the meta-analysis for different techniques for augmentation. CONCLUSIONS: Even if further studies at low risk of bias are needed, soft tissue augmentation techniques improved quantity and quality of peri-implant soft tissue. Among the augmentation procedures, CTG was associated to higher STT change compared to XCM.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.031 |
| Bibliometrics | 0.007 | 0.007 |
| 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".