Soft tissue augmentation applying a collagenated porcine dermal matrix during second stage surgery: A prospective multicenter case series
Bibliographic record
Abstract
BACKGROUND: The achievement and preservation of an adequate amount of soft tissue around implants is a critical factor for the prognosis of the treatment. PURPOSE: To evaluate the effectiveness of a porcine dermal matrix applied during second stage implant surgery for horizontal soft tissue augmentation and preservation of dimensional stability. MATERIALS AND METHODS: Twenty patients (mean age 50.2 ± 11.9 [SD] years) candidate to implant therapy and requiring soft tissue augmentation were recruited in four centers. Augmentation was performed in 24 cases. A porcine dermal matrix was placed into a buccal split-thickness pouch during uncovering surgery. Silicone impressions were taken before surgery (T0), 2 weeks later at suture removal (T2), 6 months (T3), and 24 months (T4) post augmentation. Dimensional changes of soft tissue were evaluated using superimposition of digitalized study casts. RESULTS: Nineteen patients (23 implants) could be evaluated at 6 months and 13 patients (17 implants) at 24 months. After 6-month follow-up, there was a significant dimensional gain respect to baseline, averaging 0.83 ± 0.64 mm (P < .01). This did not change significantly at 24 months (0.77 ± 0.65 mm, P = .19). The gain was >0.5 mm in 65.2% and 64.7% of the cases, respectively. Soft tissue shrinkage averaged 34.2% ± 77.0% from T2 to T3 (P < .01) and did not change thereafter (P = .39). Shrinkage was more consistent in the posterior mandible than in the maxilla, but not significantly (P = .23 at 6-month and .36 at 24-month). No adverse events occurred. CONCLUSION: Within the limitations of this prospective case series, the use of a porcine dermal matrix may provide consistent soft tissue augmentation that maintains up to 24-month follow-up, although graft shrinkage may occur in the first 6 months, depending on the location of 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".