Acellular dermal matrix and subepithelial connective tissue grafts for root coverage: A systematic review
Bibliographic record
Abstract
BACKGROUND: The aim of this systematic review was to evaluate whether patients with gingival recession would benefit from an acellular dermal matrix graft (ADMG) in ways that are comparable to the gold standard of the subepithelial connective tissue graft (SCTG). MATERIALS AND METHODS: A systematic review and meta-analysis comparing ADMG to SCTG for the treatment of Miller Class I and II recession defects was conducted according to PRISMA guidelines. PubMed, Excerpta Medica Database, and Cochrane Central Register of Controlled Trials databases were searched up to March 2016 for controlled trials with minimum 6 months duration. The primary outcome was root coverage; secondary outcomes included attachment level change, keratinized tissue (KT) change, and patient-based outcomes. Both authors independently assessed the quality of each included trial and extracted the relevant data. RESULTS: From 158 potential titles, 17 controlled trials were included in the meta-analysis. There were no differences between ADMG and SCTG for mean root coverage, percent root coverage, and clinical attachment level gain. ADMG was statistically better than SCTG for gain in width of KT (-0.43 mm; 95% confidence interval: -0.72, -0.15). Only one study compared patient-based outcomes. CONCLUSION: This review found that an ADMG would be a suitable root coverage substitute for an SCTG when avoidance of the second surgical site is preferred.
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.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.005 | 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 | 0.001 |
| 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".