Safety and efficacy of a novel, gradually anodized dental implant surface: A study in Yucatan mini pigs
Bibliographic record
Abstract
BACKGROUND: A newly developed dental implant system combining advancements in surface chemistry, topography, nanostructure, color, and surface energy aims to address biological challenges and expand clinical applications. PURPOSE: To assess the short- and long-term safety and efficacy of a novel, gradually anodized dental implant surface/anodized abutment. MATERIALS AND METHODS: Twenty-four Yucatan mini pigs (20-24 months old) received two dental implants in each jaw quadrant. Each site was randomized to receive either a commercially available anodized implant/machined abutment or a gradually anodized implant/anodized abutment with a protective layer. Animals were euthanized at 3, 6, and 13 weeks. Microcomputed tomography and histological analyses were performed. RESULTS: No significant histological differences in inflammation scores, epithelium length, bone-to-implant contact, or bone density were observed between groups for any healing time. Mucosal height was significantly higher at 3 weeks for controls (Δ = 0.2 mm); no differences were observed at 6 and 13 weeks. No significant differences in radiographic bone volume, bone-to-implant contact, trabecular thickness, and crestal bone levels were observed, irrespective of healing time. Trabecular spacing was borderline significant at 3 weeks in favor of the test implant sites; no differences were observed at 6 weeks. No significant differences were observed between experimental groups at 13 weeks. CONCLUSIONS: The new implant system yielded results comparable to a commercially available predicate device.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".