Bone Healing with or without Platelet‐Rich Plasma around Four Different Dental Implant Surfaces in Beagle Dogs
Bibliographic record
Abstract
PURPOSE: Surface development is one of the major aims in dental implant engineering. Additive application of substances could possibly improve the new bone formation around dental implants. The present study evaluated the bone reaction on four different implant surfaces with or without platelet-rich plasma (PRP). MATERIALS AND METHODS: Four self-tapping titanium screw implants (Brånemark MK III [Nobel Biocare, Göteborg, Sweden], Osseotite [3i, Miami, FL, USA], Xive [Densply Friadent, Mannheim, Germany], and Compress [IGfZ eG, Diez, Germany]) with different surfaces were inserted in each hemimandible of 12 female beagle dogs; the implant positions and the application of PRP were randomized. After intravital fluorochrome staining, sacrifices and biopsies harvesting were performed after 6 weeks (five dogs; one dog died before) and 12 weeks (six dogs) and the respective specimens were analyzed. RESULTS: The only significant difference in bone remodeling was found for the Compress implants with increased bone formation compared with the Brånemark implants at 12 weeks (sign test, p = .03). Comparing the histological and histomorphometric specimens of all other implant surfaces with respect to peri-implant bone remodeling and the resulting bone-implant contact rates (BICRs), no statistically significant differences were seen in the PRP or non-PRP groups (sign test, all p values ≥ .063). CONCLUSIONS: This study found no significant differences in the BICR for roughened implant surfaces compared with machined surfaces. In this animal model, the addition of PRP did not demonstrate evidence of faster bone formation or the resulting BICR.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".