Efficacy and safety of corticosteroids, hyaluronic acid, and PRP and combination therapy for knee osteoarthritis: a systematic review and network meta-analysis
Bibliographic record
Abstract
OBJECTIVE: There are many injectable treatments for knee osteoarthritis with different characteristics and effects, the aim is to understand which one can lead to better and safer results. METHODS: The PRISMA principles were followed when doing the literature search. Web of Science databases, Embase, the Cochrane Library, PubMed, and the Wanfang database were searched to identified randomized controlled trials that assessed the efficacy of corticosteroids (CSC), platelet-rich plasma (PRP), hyaluronic acid (HA), and combination therapy in treating KOA. Risk of bias was assessed using the relevant Cochrane tools (version 1.0). The outcome measure included the visual analog scale (VAS) score, the Western Ontario and McMaster Universities Osteoarthritis (WOMAC) score, and treatment-related adverse events. The network meta-analysis was performed using STATA17 software and a Bayesian stratified random effects model. RESULTS: Network meta-analysis using the Bayesian random-effects model revealed 35 studies with 3104 participants. PRP showed the best WOMAC score at a 3-month follow-up, followed by PRP + HA, HA, placebo, and CSC; PRP + HA scored the highest VAS, followed by PRP, CSC, HA, and placebo. PRP, CSC, HA, and placebo had the highest WOMAC scores six months following treatment; PRP + HA showed the best VAS scores. PRP showed the best WOMAC score at 12 months, followed by PRP + HA, HA, placebo, and CSC; The best VAS score was obtained with PRP, followed by PRP + HA, HA, and CSC. No therapy demonstrated a rise in adverse events linked to the treatment in terms of safety. CONCLUSIONS: The current study found that PRP and PRP + HA were the most successful in improving function and alleviating pain after 3, 6, and 12 months of follow-up. CSC, HA, PRP, and combination therapy did not result in an increase in the incidence of treatment-related side events as compared to placebo.
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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 | 0.022 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.041 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".