The Role of Platelet-Rich Plasma (PRP) in the Treatment of Patellofemoral Arthritis and Anterior Knee Pain: A Systematic Review
Bibliographic record
Abstract
Patellofemoral osteoarthritis (OA) and chondromalacia patellae (CMP) are common and disabling conditions that significantly affect physical performance and quality of life. Despite the great deal of scientific research on the subject, there is limited evidence regarding the outcome of nonoperative interventional procedures. Platelet-rich plasma (PRP) has demonstrated positive results for tibiofemoral knee osteoarthritis, but its role in anterior knee pain (AKP) remains unclear. The aim of this study was to review the evidence on the efficacy (clinical and radiological) and safety of PRP in patients suffering from patellofemoral OA, CMP, and AKP. Medline/Pubmed, Web of Science, and Scopus databases were systematically searched up to June 2025 to identify all the available relevant studies. Five studies, including 146 patients, fulfilled the eligibility criteria and were included in the systematic review. Although there was a statistically significant improvement in clinical setting, radiologic evidence of cartilage regeneration was limited and uncertain. Specifically, the pooled analysis revealed an improvement of the Visual Analogue Scale from 6.7 to 2.1 (p < 0.001), the Western Ontario and McMaster Universities Osteoarthritis Index score from 24 to 10.3 (p < 0.001), the Oxford score from 35.1 to 37.4 (p < 0.001), the Kujala score from 71 to 83 (p < 0.001), and the Tegner/Lysholm score from 65.3 to 86.5 (p < 0.001). Well-designed and appropriately powered randomized trials with imaging endpoints are needed to validate the efficacy of PRP administration in PFA, CMP, and AKP and refine patient selection criteria.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".