Platelet-Rich Plasma in Aesthetic Dermatology: Current Evidence and Future Directions
Bibliographic record
Abstract
Platelet-rich plasma (PRP) has emerged as a promising treatment in aesthetic dermatology. This systematic review aims to evaluate the current evidence for PRP applications in skin rejuvenation, hair restoration, wound healing, and fat grafting. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive literature search was conducted across multiple databases and 13 studies meeting the inclusion criteria were selected for review. The Mixed Methods Appraisal Tool (MMAT) was used to assess the quality of included studies. The reviewed studies consistently reported positive outcomes for PRP across various applications. In skin rejuvenation, significant improvements in collagen density and overall skin appearance were observed. For hair restoration, studies showed mean increases of 18-27.7 hairs/cm² in treated areas. PRP demonstrated efficacy in accelerating wound healing across various wound types, including chronic ulcers. In fat grafting, PRP was associated with improved graft survival and integration. Patient satisfaction was generally high across all applications. However, there was significant heterogeneity in PRP preparation methods and treatment protocols among studies. This systematic review provides evidence supporting the efficacy of PRP in aesthetic dermatology, particularly in skin rejuvenation, hair restoration, wound healing, and fat grafting. PRP demonstrates a favorable safety profile across applications. However, the variability in study designs and PRP protocols highlights the need for standardization. Future research should focus on large-scale randomized controlled trials with standardized protocols and longer follow-up periods to solidify the evidence base for PRP in aesthetic dermatology.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".