Crestal Bone Stability around Implants with Horizontally Matching Connection after Soft Tissue Thickening: A Prospective Clinical Trial
Bibliographic record
Abstract
BACKGROUND: It has been shown that thin mucosal tissues may be an important factor in crestal bone loss etiology. Thus, it is possible that mucosal tissue thickening with allogenic membrane might reduce crestal bone loss. PURPOSE: The purpose of this study was to evaluate how implants with traditional connection maintain crestal bone level after soft tissue thickening with allogenic membrane. MATERIALS AND METHODS: One hundred three patients received 103 internal hex implants of 4.6 mm diameter with regular connection. According to gingiva thickness, patients were assigned into A (thin tissues, n = 34), B (thin, thickened with allogenic membrane, n = 35), and C group (thick tissues, n = 34). Groups A and C had one-stage approach, and in group B, implants were placed in two stages. Radiographic examination was performed after implant placement, 2 months after healing, after restoration, and after 1-year follow-up. Crestal bone loss was calculated medially and distally. Significance was set to 0.05. RESULTS: After 1-year follow-up, implants in group A had 1.65 ± 0.08-mm bone loss mesially and 1.81 ± 0.06 mm distally. Group B had 0.31 ± 0.05 mm mesially and 0.34 ± 0.05 mm distally. C group implants experienced bone loss of 0.44 ± 0.06 mm mesially and 0.47 ± 0.07 mm distally. Differences between A and B, and A and C were significant (p = .000) both mesially and distally, whereas differences between B and C were not significant mesially (p = .166) and distally (p = .255). CONCLUSIONS: It can be concluded that thin mucosal tissues may cause early crestal bone loss, but their thickening with allogenic membrane may significantly reduce bone resorption. Implants in naturally thick soft tissues experienced minor bone 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 | 0.004 | 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) | 0.003 | 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".