The Effect of Platelet Rich Plasma Frequency On Early Stage Knee Osteoarthritis
Bibliographic record
Abstract
ABSTRACT Introduction: Platelet rich plasma (PRP) revealed quite satisfying results for early knee osteoarthritis (OA) especially in younger ages by decreasing pain and improving function and quality of life. However, what becomes the ideal dose and how often it should be performed, the effectiveness of intra-articular PRP application in different stages of OA are the main topics discussed. There are few studies in the literature comparing the efficacy of PRP with the administration dose. The primary aim of this study was to compare the efficacy of different doses of PRP in primary knee OA and to determine the ideal treatment modality. Material and methods: 174 patients who met the criteria were evaluated retrospectively between January 2016 and April 2017. The patients were divided into 3 groups according to doses. Western Ontario and McMaster University Arthritis Index (WOMAC) scores, International Knee Documentation Committee (IKDC) scores and 100 mm Visual Analogue Scale (100 mm VAS) scores were evaluated before treatment, 6 months and 12 months after treatment. Satisfaction status and treatment-related complications were examined at 12 months after treatment. Results: At the end of the follow-up, a significant improvement was observed in all three groups compared to the pre-treatment values. (p <0.05). Significant improvement was observed in pain scores (p <0.001) and functional scores (p <0.001) in all three injection groups. No significant difference was observed between twice or thrice applications (p >0.05). The clinical and functional results of one application was significantly lower than the other groups (p <0.05). Conclusion: We think that single dose therapy is less sufficient in effect, thus the appropriate method of treatment is at least 2 doses of PRP.
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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".