Histological Analysis of the Ankylos Peri-implant Soft Tissues in a Dog Model
Bibliographic record
Abstract
PURPOSE: The importance of the soft tissue-implant interface is enhanced by the presence of a microgap between the implant and the abutment, which represents a contamination site for bacteria. The aim of this study was to investigate the interface between the Ankylos gap-free implant system and the surrounding soft tissues in a dog model. MATERIALS AND METHODS: Six Labrador dogs were included in the study and two Ankylos implants were inserted per dog. The dogs were killed 6 months after abutment placement without functional loading and without plaque control. The implants were analysed histologically by scanning electron microscopy, light microscopy, and histomorphometry. RESULTS: Some sections exhibited histologic signs of a mild inflammation. The connective tissue between the most apical epithelial cells of the junctional epithelium and the alveolar crest was characterized by collagen fibers running from the periosteum and the alveolar crest toward the oral epithelium and, in front of the cone-shaped abutment, by a narrow zone of extracellular matrix with a few collagen fibers. CONCLUSION: Compared with results obtained in other studies using different types of implant (Astra, Bränemark, ITI), the Ankylos implant showed a higher length and a larger width of connective tissue contact as well as a shorter epithelial downgrowth. The absence of a microgap in the Ankylos system could explain the histologic mild inflammation in the connective tissue.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".