Influence of Vertical Soft Tissue Thickness on Crestal Bone Changes Around Implants with Platform Switching: A Comparative Clinical Study
Bibliographic record
Abstract
BACKGROUND: Numerous studies have shown the superiority of platform-switched implants in preserving crestal bone as compared with platform-matched implants. However, the influence of initial soft tissue thickness on development of crestal bone loss has not been addressed in previous studies; thus, further research is needed. PURPOSE: To evaluate crestal bone levels around platform-switched implants placed in thin and thick mucosal tissue. MATERIALS AND METHODS: Eighty patients (38 male and 42 female, mean age 44 ± 3.34 years) received 80 bone-level implants of 4.1 mm in diameter with platform switching (Institut Straumann AG, Basel, Switzerland). Tissue thickness was measured, and cases were distributed to Group 1, with thin soft tissue (2 mm or less, n = 40), and Group 2, with thick tissue (more than 2 mm, n = 40). Implants were placed with a one-stage approach and restored with screw-retained restorations. Radiographic examination was performed after implant placement, 2 months after healing, after restoration, and at 1-year follow-up post-reconstruction. Crestal bone loss was calculated. The Mann-Whitney U-test was applied, and significance was set to p ≤ .05. RESULTS: Implants in Group 1 (thin tissue) showed 0.79 mm of bone loss after 2 months. After 1-year follow-up, bone loss was 1.17 mm. Implants in Group 2 (thick tissue) showed bone loss of 0.17 mm after 2 months of implant placement and 0.21 mm after 1-year follow-up. The differences between groups were significant (p < .001) at both time points. CONCLUSIONS: It can be concluded that platform switching does not prevent crestal bone loss if, at the time of implant placement, mucosal tissue is thin. In thick soft tissue, use of platform-switched implants maintained crestal bone level with minimal remodeling.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".