Vestibuloplasty: Porcine Collagen Matrix Versus Free Gingival Graft: A Clinical and Histologic Study
Bibliographic record
Abstract
BACKGROUND: A free gingival graft (FGG) is currently the gold standard for augmenting small areas of keratinized mucosa. The porcine collagen matrix (CM) represents an alternative to autologous tissue harvesting. This study aims to compare the CM versus FGGs for augmenting keratinized peri-implant mucosa based on clinical and histologic evaluations. METHODS: The study included 14 patients who underwent a vestibuloplasty with either a FGG from the palate (n = 7) or the CM (n = 7). An implant-fixed vestibular retention splint was inserted for 30 days. Follow-up examinations were performed at 4, 10, 30, and 90 days after surgery. Width of keratinized mucosa was measured in the region of each implant (days 10, 30, and 90). After 90 days, a biopsy was harvested for histologic and immunohistologic analyses. To characterize newly formed soft tissue, the authors stained for tissue-and differentiation-specific markers, cytokeratin (CK) 5/6, 13, and 14, to detect presence or absence of keratinization. RESULTS: The groups showed similar healing, with increased peri-implant keratinized mucosa. The CM group had overall significantly shorter operation times than the FGG group. Both groups showed similar overall shrinkage (32.98% CM versus 28.35% FGG). All biopsies showed a multilayered, keratinized, squamous epithelium. CKs 5/6 and 14 were detected in the basal and suprabasal layers, and spots of CK 13 were detected in the suprabasal layer. CONCLUSIONS: During the whole observation period, both groups showed comparable clinical and histologic outcomes. Within the limitations of the present study, CM seems to be a promising alternative for the regeneration of keratinized mucosa without tissue harvesting. Comparative long-term studies are needed to investigate changes over time.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".