Assessing Bone Regeneration with T-PRF and L-PRF: Micro-CT Study
Bibliographic record
Abstract
The purpose of this study is to compare the efficacy of locally applied Titanium-Prepared Platelet-Rich Fibrin(T-PRF) and Leukocyte-Platelet Rich Fibrin (L-PRF) in bone defect healing through a micro-CT analysis and histopathological examination in rabbit models. Eight male New Zealand rabbits aged 4-6 months were subjected to surgery to create circular bicortical defects with a 6 mm diameter. The defects were treated with either T-PRF, L-PRF, or saline solution as a control. Micro-CT imaging with a Bruker Skyscan 1272 system was utilized to evaluate bone regeneration, followed by histological examination after sacrifice. Statistical analysis was performed to determine significant differences among the groups. Analysis of micro-CT data revealed significant differences among the experimental groups in terms of bone volume, trabecular thickness, trabecular number, connectivity, and connectivity density (p<0.05). Both T-PRF and L-PRF groups exhibited improved bone parameters compared to the control group, with the L-PRF group demonstrating even better outcomes. However, trabecular separation and bone surface area to volume ratio did not show significant differences among the groups (p>0.05). Histological examination indicated advanced healing stages in the L-PRF group, suggesting the efficacy of both T-PRF and L-PRF in bone regeneration, with L-PRF showing superior effects. Due to the surface modifications of titanium tubes used in the preparation protocol of T-PRF extracts, sterilization conditions, and environmental factors, they can influence the quality of the obtained extract. Considering the challenges in standardizing the factors in the preparation protocol of T-PRF, it is recommended to apply L-PRF for the healing of bone defects.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".