Evaluating the Efficacy of Platelet-Rich Plasma in Treating Primary Knee Osteoarthritis: A Prospective Interventional Study
Bibliographic record
Abstract
INTRODUCTION: Knee osteoarthritis (OA) is a prevalent degenerative joint disorder causing pain, stiffness, and reduced function, significantly impacting the quality of life. Current treatments mainly provide symptomatic relief, with limited efficacy in halting disease progression. Platelet-rich plasma (PRP), a biological therapy rich in growth factors, has gained attention as a potential treatment for knee OA due to its regenerative properties. This study evaluates the efficacy of PRP in managing primary knee OA. METHODOLOGY: This prospective interventional study included 100 patients diagnosed with primary knee OA, categorized using the Kellgren-Lawrence grading scale. Leukocyte-reduced PRP was prepared using the double-spin method and injected into the knee joint. The efficacy of PRP was assessed using the Western Ontario and McMaster Universities Arthritis (WOMAC) Index and Visual Analogue Scale (VAS) pain scores at six weeks, three months, and six months post-injection. Statistical analysis was performed using SPSS version 25.0, with significance set at p<0.05. RESULTS: Significant improvements in the WOMAC and VAS scores were observed at all-time points post-PRP injection. The WOMAC score decreased from a baseline of 81.06 to 63.52 at six months (p < 0.001), and the VAS score reduced from 7.53 to 3.09 (p < 0.001). PRP was more effective in patients with lower body mass index (BMI) and less severe OA (Grades 1 and 2). Adverse events were mild, with 18% reporting mild pain or swelling. CONCLUSION: PRP therapy significantly improves pain and function in patients with primary knee OA, particularly in early-stage disease. The treatment is generally safe, with minor adverse effects. PRP presents a promising non-surgical option, especially for those seeking to delay or avoid knee arthroplasty.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".