Intra-articular platelet-rich plasma versus hyaluronic acid in the treatment of knee osteoarthritis: a meta-analysis
Bibliographic record
Abstract
Purpose: Platelet-rich plasma (PRP) and hyaluronic acid (HA) have been increasingly used in recent years to treat knee osteoarthritis (OA). However, whether PRP is superior to HA is controversial. Methods: We conducted an electronic search of PubMed, Embase, ScienceDirect, and Cochrane library. The pooled data were analyzed using RevMan 5.1. Results: Three prospective and ten randomized trials were identified. PRP injections reduced pain more effectively than HA injections in OA of the knee at 6 months (mean difference [MD]=-14.18; 95% confidence interval [CI]: -26.12 to -2.23; P =0.02; I 2 =95%) and 12 months (MD=-15.25; 95% CI: -22.17 to -8.32; P <0.01; I 2 =81%) of follow-up evaluated by Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score, while the VAS showed no significant difference at 3 months (MD=-0.98; 95% CI: -2.55 to 0.59; P =0.22; I 2 =90%) and 6 months (MD=-0.82; 95% CI: -1.80 to 0.16; P =0.1; I 2 =83%). Additionally, similar results were observed for the function recovery according to the WOMAC function score and EuroQol-visual analog scales. Conclusion: The intra-articular injection of PRP was not obviously superior to HA in knee OA. Due to the limited quality and data of the evidence currently available, more high-quality randomized controlled trials are required. Keywords: knee, osteoarthritis, hyaluronic acid, platelet-rich plasma
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.045 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".