A Prospective and Comparative Study of the Effectiveness of Intra-articular Injection of Platelet-rich Plasma versus Hyaluronic Acid for Osteoarthritis of Knee Joint
Bibliographic record
Abstract
Abstract Background: Osteoarthritis (OA) of the knee joint is a degenerative disease characterized by pain, decreased range of motion, and cartilaginous damage leading to disability. Nowadays, it is becoming more likely in the younger population due to sedentary and unhealthy lifestyles. Weight reduction and anti-inflammatory drugs are conservative treatment options, but newer therapies such as intra-articular injections of hyaluronic acid (HA) and platelet-rich plasma (PRP) are also being used for patients who are not responding to conservative therapies before surgical intervention. Aim and Objective: This study was designed to compare the efficacy and safety of intra-articular injections of HA and PRP in patients diagnosed with knee OA. Materials and Methods: This study included 86 patients fulfilling the inclusion and exclusion criteria, out of which 43, selected randomly, were injected with intra-articular PRP, and the others were injected with HA. Then, they were followed up at 1, 3, and 6 months, and the scores of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and Visual Analog Scale (VAS) scales were used to compare the improvements compared to the baseline data at the first visit. Results: The study showed that PRP had better efficacy over HA in reducing WOMAC and VAS scores, and none of the two groups showed any adverse effects over 6 months. Conclusion: Our study demonstrates that PRP is more effective than HA, with both being safe in reducing pain and stiffness and improving physical function in OA patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".