Clinical and Radiological Benefits of Intra-Articular Platelet Rich Plasma (PRP) Injection in Patients with Knee Osteoarthritis
Bibliographic record
Abstract
Background: Although several studies had investigated the possibility of being effective in treating osteoarthritis, the use of PRP as a line of treatment in such cases is not yet approved.Aim of the Work: To assess the potential clinical and radiological benefits of intra-articular PRP injection in patients with knee osteoarthritis.Patients and Method: The study included 45 knee osteoarthritis patients following-up at the rheumatology clinic, Ain Shams University hospitals. Demographic, clinical data including the visual analogue scale (VAS), and Western Ontario and McMaster Universities (WOMAC) index, Magnetic Resonance Imaging (MRI) knee joint were analyzed. Intraarticular knee injection of PRP for 6 consecutive months was done. Patients were reassessed clinically and radiologically after 6 months from the last injection.Results: There was 39 (86.7 %) females, 6 (13.3 %) males. There was a statistically significant reduction in VAS & WOMAC scores after PRP injection (P <0.001). MRI showed statistically significant decrease in the subchondral bone marrow lesions (P 0.004) and statistically significant increase in the patellar cartilage volume (P 0.02), non-significant decrease in inter-condylar synovitis (P 0.51). The patient's age and disease duration were significantly negatively correlated only with the VAS improvement percent (P 0.04, 0.03 respectively), BMI didn’t show any significance with both scores. There was no major side effects of PRP injection like infection or bleeding. Minimal pain at site of injection reported by some patients.Conclusions: The use of Intraarticular PRP injection improves the clinical and radiological outcomes of knee osteoarthritis without major adverse effects
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".