The Influence of Initial Soft Tissue Thickness on Peri‐Implant Bone Remodeling
Bibliographic record
Abstract
AIM: To elucidate the influence of initial soft tissue thickness on peri-implant bone remodeling. The research hypothesis was that implants installed in patients or at sites with thin mucosal tissues would show increased peri-implant bone loss. MATERIAL AND METHODS: 79 edentulous patients were consecutively treated with two non-splinted implants supporting an overdenture in the mandible. During recall-visits, peri-implant health was determined by means of probing pocket depth and the modified plaque/bleeding index. Digital peri-apical radiographs were taken from individual implants. Bone level changes were measured from a reference point (lower border of the smooth implant collar) to the marginal bone-to-implant contact level. The linear mixed-effect model analysis was adopted to analyze the influence of clinical parameters and transmucosal abutment height on peri-implant bone loss. RESULTS: 67 patients attended the 1-year and 66 the 2-year recall-visit. Mean bone level changes were 0.89 mm (SD 0.62) and 0.90 mm (SD 0.66), plaque scores 0.82 (SD 0.94) and 0.87 (SD 0.92), bleeding scores 0.46 (SD 0.68) and 0.56 (SD 0.72) and PPD 1.65 mm (SD 0.60) and 1.78 mm (SD 0.59) after 1 year and 2 years respectively. The linear mixed-effect model revealed increasing bone level changes with decreasing abutment heights. Peri-implant bone level changes were significantly higher for implants with abutments of <2 mm (1.17 mm, p < .01; 1.23 mm, p < .01), 2 mm (0.86 mm, p < .01; 1.03 mm, p < .01) or 3 mm (0.38 mm, p = .046; 0.41 mm, p = .044) compared to ≥4 mm-abutments (bone level changes set to zero as reference value) both after 1 year and 2 years and bone level changes were significantly influenced by probing pocket depth (p < .01, p < .01), but not by plaque (p = .31, p = .09) and bleeding scores (p = .30, p = .40). CONCLUSION: The present study suggests that implants with lower abutments, reflecting the initial gingival thickness, lose more peri-implant bone, possibly by a re-establishment of the biological width.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".