Efficacy of maxillary tuberosity connective tissue grafts in periodontal and peri-implant soft tissue procedures: a systematic review
Bibliographic record
Abstract
The objective of this systematic review was to assess the efficacy of maxillary tuberosity connective tissue grafts (MT-CTGs) in periodontal plastic surgeries at tooth and implant sites. An electronic search of literature in OVID, Embase, Cochrane and Scopus databases and a manual search up to August 2022 were performed to identify clinical studies at all levels of evidence with a minimum 3 month follow-up. Out of 880 potential publications, 10 studies were included, which included randomized controlled trials (RCTs), cohort studies and case reports. Due to study heterogeneity, a meta-analysis was not performed. Risk of bias was assessed with the Cochrane Risk of Bias 2 tool, the Newcastle-Ottawa Scale and the JBI Critical Appraisal checklist. MT- CTGs were more commonly utilized for peri-implant soft tissue augmentation, with keratinized mucosa thickness gain of 3-4 mm. Favourable gingival recession and mucosal dehiscence coverage outcomes, and satisfactory aesthetic ratings were reported though untoward hyperplastic tissue reactions at treated sites have also been documented. The limited evidence suggests MT-CTGs to be a sound soft tissue graft choice which may perform as well as lateral palate CTGs in periodontal soft tissue surgeries. Their true effect is yet to be determined with more well-designed long-term RCTs.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".