Effect of Leukocyte‐ and Platelet‐Rich Fibrin (<scp>L</scp>‐<scp>PRF</scp>) on Bone Regeneration: A Study in Rabbits
Bibliographic record
Abstract
BACKGROUND: The positive effect of leukocyte- and platelet-rich fibrin (L-PRF) on osteogenesis has been widely described in vitro. However, clinical and preclinical studies are very little and controversial in demonstrating a significant beneficial effect of L-PRF in bone regeneration. PURPOSE: The goal of the present study was to compare the potential effect of L-PRF in a standardized model. MATERIALS AND METHODS: A total of 72 hemispheres were implanted on the calvaria of 18 rabbits and filled with three different space fillers: L-PRF, bovine hydroxyapatite (BHA), BHA + L-PRF, and an empty hemisphere was used as control. Six rabbits were sacrificed at three distinct time points: 1 week, 5 weeks, and 12 weeks. Histological and histomorphometrical analyses were carried out. RESULTS: At the early phase of bone regeneration (1 week), from a descriptive analysis, a higher proportion of connective tissue colonized the regeneration chamber in the two groups containing BHA particles. Nevertheless, no statistical differences were found within the four groups in terms of bone quantity and quality at each timepoint (p = .3623). CONCLUSIONS: According to the present study, L-PRF does not seem to provide any additional effect on the kinetics, quality, and quantity of bone in the present model of guided bone regeneration.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".