Efficacy and Safety of Platelet-Rich Plasma for Patients With Meniscal Injury: A Systematic Review and Meta-analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background: Platelet-rich plasma (PRP) is widely used to promote healing and improve function in various musculoskeletal injuries. However, the efficacy and safety of PRP for meniscal injury remain unclear. Purpose: To evaluate the effects of PRP in patients with meniscal injury. Study Design: Systematic review; Level of evidence, 2. Methods: A comprehensive search was conducted in PubMed, Embase, Cochrane Library, Web of Science, Wanfang, and CNKI databases. Randomized controlled trials comparing PRP with placebo or no additional treatment in adult patients with meniscal injury were included. Outcome measures included the visual analog scale for knee pain, Lysholm score, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Knee injury and Osteoarthritis Outcome Score (KOOS), treatment failure, and complications. Data were pooled using a random-effects model by incorporating the influence of heterogeneity. Results: This meta-analysis included 18 randomized controlled trials involving a total of 1143 patients. The mean follow-up duration ranged from 3 to 12 months. PRP significantly reduced knee pain (mean difference [MD], -0.73; 95% CI, -0.91 to -0.55) and improved knee function, as indicated by higher Lysholm scores (MD, 6.77; 95% CI, 5.35 to 8.20) and KOOS (MD, 4.34; 95% CI, 1.35 to 7.32), and lower WOMAC scores (MD, -5.33; 95% CI, -8.10 to -2.56). Subgroup analyses suggested similar results in patients with and without concurrent knee osteoarthritis, with single and multiple PRP injection, and with follow-up duration of <12 and ≥12 months. In addition, PRP also reduced treatment failure rates (odds ratio, 0.26; 95% CI, 0.12 to 0.59), with no significant difference in complications. Conclusion: PRP is effective in reducing pain and improving knee function in patients with meniscal injury. Registration: CRD42024601679 (PROSPERO).
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.035 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 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".