Comparing the efficacy of dual Platelet‐Rich Plasma (PRP) and Hyaluronic Acid (HA) therapy with PRP‐alone therapy in the treatment of knee osteoarthritis: a systematic review and meta‐analysis
Bibliographic record
Abstract
PURPOSE: This study aims to compare the efficacy of a dual therapy of Platelet-Rich Plasma (PRP) and Hyaluronic Acid (HA) compared with PRP-alone therapy in the treatment of knee osteoarthritis (KOA). METHODS: PubMed, Embase, CINAHL, SCOPUS, Cochrane Library, grey literature and bibliographic references were searched from inception to January 2021. Only randomized controlled trials (RCTs) and retrospective cohort studies comparing the effect of PRP and HA versus PRP-alone therapy for KOA were included. Literature retrieval and data extraction were conducted by three independent reviewers. Pooled analysis of Visual Analogue Scale (VAS), Western Ontario and McMaster Universities Arthritis Index (WOMAC), International Knee Documentation Committee (IKDC) scores and adverse events were conducted. RESULTS: Ten studies (7 RCTs, 3 cohort studies) involving 983 patients were covered. Dual PRP and HA therapy resulted in significant reduction in VAS compared to PRP-alone therapy at 4-6 weeks (P < 0.00001) and 12 months (P < 0.00001). Dual therapy resulted in better WOMAC score improvement at 3 (P = 0.02), 6 (P = 0.05) and 12 months (P < 0.0001) compared to PRP-alone therapy. The IKDC score for dual therapy was also higher at 6 months compared to PRP-alone therapy (P = 0.007). Regarding adverse events, dual therapy was generally safer than PRP-alone therapy (P = 0.02). CONCLUSION: While there is a paucity of large high-quality Level I studies, current best evidence suggests that dual therapy with PRP and HA for KOA may be effective at providing pain relief and improvement in function up to 1 year following administration. LEVEL OF EVIDENCE: II.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.029 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".