The Effectiveness of Platelet Rich Fibrin in Alveolar Ridge Reconstructive or Guided Bone Regenerative Procedures: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: Clinical studies have shown favorable outcomes following use of platelet rich fibrin (PRF), either alone or in conjunction with biomaterials for alveolar ridge reconstruction (ARR) or guided bone regeneration (GBR) . While PRF application accelerates wound healing and reduces postoperative discomfort, its effects on the alveolar bone gain, as part of ARR or GBR is less clear. Therefore, this study aims to investigate the clinical effectiveness of PRF when used in ARR or GBR, as well as postoperative discomfort following these procedures. SOURCES: A systematic search using the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) method was performed to include database searches from MEDLINE (OVID interface, 1946 onwards), EMBASE (OVID interface, 1974 onwards) and Global Health (OVID interface, 1973 onwards). DATA: Of the 74 studies initially identified, 7 studies were included for the systematic review, including 6 randomized controlled trials (RCTs) and 1 cohort study. The meta-analysis showed that the incorporation of PRF as part of ARR or GBR resulted in an increase in horizontal ridge width, a reduction in the rate of resorption increase, while postoperative discomfort was the same or slightly improved. The risk of bias quality was low for only 1 out of the 6 RCTs and the Newcastle Ottawa scale assessment showed that cohort study was of high quality. CONCLUSION: PRF application in ARR or GBR is associated with increased horizonal alveolar ridge width and reduce rate of graft resorption. However, the findings related to both outcome measures were based on a limited number of studies. CLINICAL SIGNIFICANCE: PRF application can be effectively used as part of ARR or GBR to increase the horizontal ridge width and reduce the rate of graft resorption.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".