Comparative study of clinical and functional outcome between the efficacy of platelet rich plasma and hyaluronic acid injection in osteoarthritis of knee joint
Bibliographic record
Abstract
Introduction: Platelet rich plasma (PRP), a blood-derived product rich in growth factors, is a treatment for cartilage defects. PRP's use is limited due to the lack of clinical evidence. Various studies have suggested that an injection of hyaluronic acid (HA) provides better results in early osteoarthritis. Purpose: We aimed to compare clinical and functional outcomes of using hyaluronic acid versus platelet rich plasma in the treatment of osteoarthritis of the knee joint. Methods: 60 patients were included in the study. 30 were treated with HA and the other 30 with PRP. The patients were evaluated 6 months after the procedure. Patients were evaluated before and after the procedure using Visual Analogue Scale (VAS) and Western Ontario McMaster Universities Osteoarthritis Index (WOMAC). Range of motion was measured over time. Adverse events and patient satisfaction were also recorded. Results: Both groups presented a clinical improvement but significantly better results were seen in the group of patients receiving PRP injections as indicated by their WOMAC and VAS scores at a 12 week and 24 week follow-up. No severe adverse events were observed. Mild pain and effusion after the injection was seen in the PRP group. Conclusions: Our preliminary findings support the application of autologous PRP as a safe and effective method in the treatment of the initial stages of knee osteoarthritis. Significant clinical improvement was seen with 6 months of follow-up. More promising results need to be obtained in order to use it for low grade degeneration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".