The Efficiency of Platelet-Rich Plasma (PRP) in Treating Post-Burn and Surgical Scars: A Meta-Analysis Study
Bibliographic record
Abstract
Background/Objectives: Scarring frequently lowers quality of life and can have both functional and aesthetic impacts. Recently it has been debated whether platelet-rich plasma (PRP) can be used as a therapy modality for improving scar outcomes, given its abundance of cytokines and growth factors. This study attempts to methodically assess PRP’s efficacy in healing various scar types, such as burns, keloids, and postoperative scars, using randomized controlled trials (RCTs). Methods: A systematic search with PubMed, Google Scholar, Ovid Medline, Web of Science, and the Cochrane Central Register of Controlled Trials (CENTRAL) was conducted for RCTs, up to 12 October 2024, evaluating the use of PRP in scar treatment. Outcomes were assessed using validated tools, including the Patient and Observer Scar Assessment Scale (POSAS) and the Vancouver Scar Scale (VSS), with only one study using ultrasound imaging. The Cochrane Risk of Bias 2.0 tool was used to analyze potential bias. Standardized mean differences were computed for continuous outcomes, and meta-analyses were conducted with random-effects models. Results: A total of 11 RCTs involving 539 participants were included, covering 5 postoperative scars, 4 burn scars, and 2 keloid studies. Although several individual trials indicated enhancements with PRP, the pooled data revealed no statistically significant alterations in scar appearance at three months, as assessed by the Observer Scar Assessment Scale (OSAS) and VSS scores. A statistically significant enhancement in patient-reported outcomes utilizing the Patient Scar Assessment Scale (PSAS) was noted at 6 months (mean difference = −0.825; 95% CI: −1.561 to −0.090; p = 0.028), suggesting PRP’s efficacy in alleviating symptoms, including pain, itching, and stiffness. High heterogeneity was noted in several analyses, likely due to variability in PRP preparation methods, administration protocols, and scar types. Conclusions: This meta-analysis indicates that PRP may provide significant subjective enhancements in scar quality, particularly from the patient’s viewpoint. The results show the need for standardized PRP protocols, prolonged follow-up periods, and the integration of both patient-centered and objective outcome measures. PRP has significant potential in early postoperative wound healing; nevertheless, additional high-quality, long-term studies are required to clarify its role in mature or fibrotic scars.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.057 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".