Influence of Platelet‐Rich Plasma Added to Xenogeneic Bone Grafts in Periimplant Defects: A Vital Fluorescence Study in Dogs
Bibliographic record
Abstract
BACKGROUND: The use of platelet-rich plasma (PRP) has been suggested in order to increase the rate of bone deposition when sites are augmented prior to or in conjunction with dental implant placement. PURPOSE: The goal of this study was to investigate whether the addition of PRP to xenogeneic bone grafts would increase the rate of bone formation in dogs. MATERIALS AND METHODS: Ninety endosseous dental implants were inserted in the mandibles of nine hound dogs. Subsequently, mesial and distal three-wall periimplant defects were surgically created. Defects were randomly assigned to three groups: demineralized freeze-dried bone graft plus platelet-rich plasma (DFDBG plus PRP), demineralized freeze-dried bone graft alone (DFDBG), and no treatment. Postsurgically each dog received a series of three fluorescent labels for estimation of bone cell activity at baseline and during different stages of healing, with particular attention to the bone formation rate per tissue volume (BFR/TV). Animals were sacrificed at 1 month, 2 months, and 3 months, and specimens were subjected to analysis by fluorescence microscopy. Treatment effects were evaluated with analysis of variance models. RESULTS: Overall, the average BFR/TV differed by treatment although this difference approached only minimal statistical significance (p = .057). The largest difference occurred between periimplant defects treated with DFDBG only and defects that were not treated (mean percentage BFR/TV, 0.0720% vs 0.0994%). There was no evidence of an overall treatment effect (p = .27) for the mineral apposition rate (MAR) values. The data also suggest a consistent variability in the bone formation parameters among the three groups at different healing points. CONCLUSION: In this animal model the addition of PRP to xenogeneic bone grafts did not demonstrate evidence of faster bone formation during healing. However, limitations of the histologic technique possibly played a negative role in the assessment of bone formation parameters.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".