Adding Intra-Articular Growth Hormone to Platelet Rich Plasma under Ultrasound Guidance in Knee Osteoarthritis: A Comparative Double-Blind Clinical Trial
Bibliographic record
Abstract
INTRODUCTION: Intra-articular injections of platelet rich plasma (PRP) for the treatment of knee osteoarthritis have been shown to reduce pain and improve joint function. The aim of this study is to examine the joint function by adding intra-articular growth hormone to platelet rich plasma. This study was performed on the individuals with knee osteoarthritis and under ultrasound guidance. METHODS: Fifty four patients who were scheduled for ultra-sound guided intra-articular injection were enrolled in the study. The patients were randomly allocated to groups P (platelet rich plasma) and PS (platelet rich plasma and Somatropin). Group P and PS were injected with 5 mL of platelet rich plasma, and 4 IU growth hormone (Somatropin) added to platelet rich plasma, respectively. Intra-articular injection was performed in two steps; the onset of study and one month after. Knee joint function based on Western Ontario and McMaster osteoarthritis index (WOMAC) score at the baseline, 1 and 2 month later, and complications were evaluated. RESULTS: WOMAC score in both groups has been significantly reduced after injections (P = 0.030). WOMAC score reduction in group PS in first month was significantly higher than group P, but in second month 2, the difference between two groups was not significant (P = 0.235). No complication was observed. CONCLUSIONS: These results showed that adding growth hormone to platelet rich plasma for intra-articular injection improved function of the osteoarthritic knee joint in short period of time.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".