The mucosal barrier at implant abutments of different materials
Bibliographic record
Abstract
OBJECTIVE: The aim of the present study was to analyze the soft tissue barrier formed to implant abutments made of different materials. MATERIAL AND METHODS: Six Labrador dogs, about 1 year old, were used. All mandibular premolars and the first, second and third maxillary premolars were extracted. Three months later four implants (OsseoSpeed, 4.5 x 9 mm, Astra Tech Dental, Mölndal, Sweden) were placed in the edentulous premolar region on one side of the mandible and healing abutments were connected. One month later, the healing abutments were disconnected and four new abutments were placed in a randomized order. Two of the abutments were made of titanium (Ti), while the remaining abutments were made of ZrO(2) or AuPt-alloy. A 5-months plaque control program was initiated. Three months after implant surgery, the implant installation procedure and the subsequent abutment shift were repeated in the contra-lateral mandibular region. Two months later, the dogs were euthanized and biopsies containing the implant and the surrounding soft and hard peri-implant tissues were collected and prepared for histological analysis. RESULTS: It was demonstrated that the soft tissue dimensions at Ti- and ZrO(2) abutments remained stable between 2 and 5 months of healing. At Au/Pt-alloy abutment sites, however, an apical shift of the barrier epithelium and the marginal bone occurred between 2 and 5 months of healing. In addition, the 80-mum-wide connective tissue zone lateral to the Au/Pt-alloy abutments contained lower amounts of collagen and fibroblasts and larger fractions of leukocytes than the corresponding connective tissue zone of abutments made of Ti and ZrO(2). CONCLUSION: It is suggested that the soft tissue healing to abutments made of titanium and ZrO(2) is different to that at abutments made of AuPt-alloy.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".