Platelet concentrates in periodontics: a journey through platelet-rich plasma, platelet-rich fibrin and their therapeutic uses: a literature update
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review aims to provide an updated overview of platelet concentrates, particularly platelet-rich plasma (PRP) and platelet-rich fibrin (PRF), and their evolving applications in periodontal therapy. It highlights their regenerative potential, mechanisms of action, and clinical outcomes in managing periodontal defects. RECENT FINDINGS: Recent findings emphasize PRF's superiority over PRP due to its simpler preparation, sustained release of growth factors, and absence of anticoagulants, promoting enhanced tissue healing and bone regeneration. Studies support the adjunctive use of PRF in procedures like flap surgeries, intrabony defect treatment, and gingival recession coverage. Clinical evidence favors PRF as a predictable, biocompatible aid in modern periodontal regenerative strategies. SUMMARY: Its unique three-dimensional fibrin network supports cellular migration and acts as a framework for tissue regeneration, offering gradual release of growth factors like transforming growth factor-beta and platelet-derived growth factor. Advanced forms, such as injectable PRF, are developed to enhance clinical outcomes by promoting fibroblast migration and tissue regeneration. This review explores the various types of platelet concentrates, their preparation methods, and their applications in periodontal therapy. It highlights the potential of PRF in enhancing periodontal regeneration, offering a comprehensive overview of current advancements and future directions in this field.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".