Assessment of the Efficacy of Intra-Articular Platelet-Rich Plasma Injections on Functional Improvement in Early Knee Osteoarthritis: A Clinical Study
Bibliographic record
Abstract
Background: Early knee osteoarthritis (OA) is a prevalent condition that significantly impairs mobility and quality of life. Conventional treatments provide limited relief in early OA, prompting interest in regenerative therapies such as intra-articular platelet-rich plasma (PRP) injections. PRP, rich in growth factors, is hypothesized to stimulate tissue repair and improve joint function. Objective: To evaluate the efficacy of intra-articular PRP injections in improving functional outcomes and reducing symptoms in patients with early knee OA. Methods: This clinical study included 100 patients with early knee OA who received two intra-articular PRP injections at two-week intervals. Functional improvement was assessed using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and Visual Analog Scale (VAS) for pain, recorded at baseline and three months post-treatment. Data were analyzed for significant changes in scores and associations with demographic factors. Results: Significant improvements were observed in WOMAC total scores, with a mean reduction of 45% (p < 0.01). Pain severity measured by VAS decreased from 7.8 ± 1.3 at baseline to 3.5 ± 1.1 at follow-up (p < 0.01). Functional improvement was greater in younger patients (<50 years) and those with lower baseline WOMAC scores. No major complications were reported. Conclusion: Intra-articular PRP injections significantly improve pain and functional outcomes in patients with early knee OA. PRP offers a promising alternative for managing early OA symptoms, particularly in younger and less severe cases.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".