Temporal changes in WOMAC scores following intra-articular platelet-rich plasma injection in knee osteoarthritis: a prospective cohort study
Bibliographic record
Abstract
Background Knee osteoarthritis (OA) is a leading cause of disability, with limited regenerative treatment options. Platelet-rich plasma (PRP) has emerged as a promising biologic therapy. Objective To evaluate the clinical efficacy of intra-articular PRP injections in patients with knee OA using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score. Patients and methods A prospective observational study was conducted on 40 patients with Kellgren–Lawrence grade II–III knee OA. All patients received two intra-articular PRP injections at 2-week intervals. WOMAC scores were recorded at baseline and 6 months postinjection. Clinical parameters, including swelling, tenderness, warmth, and crepitus, were also assessed. Results The mean baseline WOMAC score was 40.31 ± 13.18, which significantly improved to 16.09 ± 4.22 at 6 months ( P <0.001). Significant improvements were observed in pain, stiffness, and physical function subscales. Tenderness showed significant clinical improvement ( P <0.001), while swelling, warmth, and crepitus did not reach statistical significance. Conclusion Intra-articular PRP injection significantly improves WOMAC scores and clinical symptoms in patients with moderate knee OA. PRP is a safe and effective nonsurgical option for symptom relief.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".