Connective tissue graft vs porcine collagen matrix after immediate implant placement in esthetic area: A randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: The use of connective tissue graft (CTG) with immediate implant placement and provisionalisation have shown promising results. It is not clear if the same outcome could be achieved using porcine-derived collagen matrix (PDCM) as grafting material. OBJECTIVES: This study aimed to assess the esthetic and functional outcomes of immediate temporization of immediately placed fully tapered implants combined with bone and soft tissue augmentation, using either a CTG or a PDCM, in fresh extraction sockets of the anterior sites. MATERIALS AND METHODS: Patients with a failing anterior tooth were included in this study. After extraction, they received an immediate implant with simultaneous hard and soft tissue augmentation and immediate provisional restoration. Patients were randomly assigned to one of the group. Soft tissue augmentation in the control group (CTG) consisted of a CTG, whereas PDCM was used in the test group. After 4 months, definitive restorations were delivered, and pink esthetic score (PES) was evaluated at T1, prosthetic delivery, and at 12-month follow-up (T2). In addition, crestal bone change, probing depth, bleeding on probing, plaque index, bleeding on provisional removal, and implant stability quotient were also recorded. RESULTS: A total of 45 patients received the intended treatment (22 controls and 23 tests) 45 implants totally, with no implant failures at T2. PES mean ± SD after 1 year was noted to be 12.9 ± 1.2 for the CTG group and 12.1 ± 1.3 for the PDCM group (p = 0.507). CONCLUSION: Within the limits of this trial, both treatment protocols resulted in comparable esthetic outcomes, with results showing PES >12 and stable clinical parameters after 1 year of follow-up.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".