<scp>3D</scp>‐Analysis of Peri‐Implant Soft Tissue Gain With Collagen Matrix and Connective Tissue Graft: A Randomized Control Trial
Bibliographic record
Abstract
OBJECTIVES: To compare the efficacy of the connective tissue graft (SCTG) and the collagen matrix (VXCM) in terms of soft tissue gain at the buccal site around a single implant. METHODS: The study was designed as a randomized, controlled clinical trial. This trial was registered in ClinicalTrial.gov with the identifier NCT05870774 and is accessible under the following link: https://clinicaltrials.gov/study/NCT05870774?term=NCT05870774&rank=1. This clinical trial was not registered prior to participant recruitment and randomization. Patients with a single tooth gap and horizontal soft tissue thickness deficiency were enrolled in the study. Sites were randomly allocated to the control (SCTG) or test group (VXCM: Geistlich Fibro-Gide, Geistlich Pharma AG, Wolhusen, Switzerland) to augment buccal soft tissue thickness. The primary outcome was soft tissue gain 3 months post-op. Secondary outcomes included soft tissue gain 6 months post-op, the pink aesthetic score (PES), and patient-reported outcome measures (PROMs). RESULTS: Patients' recruitment started on 28 October 2021 and ended on 25 December 2022. Thirty-two patients were enrolled and subjected to intervention. Sixteen patients were included per group. Three months post-op, soft tissue gain at the buccal site was 1.77 ± 0.61 mm in the VXCM group and 1.26 ± 0.41 mm in the SCTG group (p = 0.0003). Six months post-op, soft tissue gain was 1.11 ± 0.44 mm in the VXCM group and 1.43 ± 0.81 mm in the SCTG group (p = 0.0459). PROMs, including pain perception, favored the VXCM group. SCTG demonstrated favored results in PES. CONCLUSION: SCTG remains the gold standard for increasing soft tissue thickness in terms of the clinical result.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".