Safety and efficacy of a novel anodized abutment on soft tissue healing in Yucatan mini‐pigs
Bibliographic record
Abstract
BACKGROUND: It is well established that electrochemical anodization of implant surfaces contributes to osseointegration and long-term implant survival. Few studies have investigated its effect on soft tissue healing. PURPOSE: To evaluate the safety and efficacy of a novel abutment surface prepared by electrochemical oxidation compared to commercially available machined titanium abutments. MATERIALS AND METHODS: Twelve 16-19 months-old, Yucatan mini-pigs received three dental implants in each mandibular jaw quadrant. Each side was randomized to receive either an anodized or a machined titanium abutment. Titanium healing caps were placed on both abutments. Animals were euthanized at 6 and 13 weeks. Radiographic and histological analyses were performed. RESULTS: No significant differences were observed histologically between groups in regard to inflammation, epithelium length, mucosal height, bone-to-implant contact, or bone density for any time point. Radiographically, crestal bone level change from baseline to 6 weeks was significantly lower for anodized than machined abutments (P = 0.046); no significant differences were observed at 13 weeks (P = 0.12). CONCLUSIONS: The novel anodized abutment showed a comparable effect on soft and hard tissue healing/remodeling and inflammation reaction to standard titanium abutments. Clinical studies should confirm these findings and explore the positive radiographic results observed at the early time point.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".