Platelet-rich plasma versus hyaluronic acid in the treatment of knee osteoarthritis
Bibliographic record
Abstract
BACKGROUND: This meta-analysis focuses on the controversial efficacy and safety of platelet-rich plasma (PRP) as compared with hyaluronic acid (HA) in the clinical treatment of knee osteoarthritis. We have attempted to provide an evidence-based medicine protocol for the conservative treatment of knee osteoarthritis. In addition, we included the latest relevant literature in this meta-analysis, and a staging study was conducted to compare the therapeutic effects of PRP and HA for knee osteoarthritis over different time periods. METHODS: An online computer search with "platelet-rich plasma" and "knee osteoarthritis" as search terms was conducted in the PubMed, EMBASE, and Cochrane Library databases. We conducted a quality assessment of the retrieved literature and extracted the following indicators: visual analog scale (VAS) score, subjective International Knee Documentation Committee (IKDC) score, Western Ontario and McMaster Universities (WOMAC) score, Knee Injury and Osteoarthritis Outcome Score (KOOS), and adverse events. RevMan5.3 software was used to determine the effect sizes, and indicators were compared across studies at three different time points from the administration of treatment. RESULTS: A total of 14 randomized controlled trials (RCTs) involving 1350 patients were included. Long-term VAS, IKDC, WOMAC-Pain, WOMAC-Stiffness, WOMAC-Physical Function, and WOMAC-Total scores at each time point were higher in the PRP group than in the HA group. There were no significant differences in the remaining indicators between the two groups. CONCLUSION: Compared with HA, PRP offers obvious advantages in the conservative treatment of knee osteoarthritis. Treatment with PRP can reduce long-term pain and improve knee joint function with no additional risks. Therefore, PRP can be widely used for the conservative treatment of knee osteoarthritis.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.027 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".