Exploring the Superior Efficacy of Hyaluronic Acid and Platelet-Rich Plasma Co-Administration Over Hyaluronic Acid Alone in Pain Reduction and Functional Status Improvement of Knee Osteoarthritis Patients
Bibliographic record
Abstract
Objective: This study aimed to determine whether concurrent injection of hyaluronic acid (HA) with platelet-rich plasma (PRP) would enhance therapeutic effectiveness compared to HA alone in the management of knee osteoarthritis (OA) over a 12-month follow-up. Methodology: A prospective, comparative study was conducted in department of orthopedic Nawaz Sharif Medical College / Aziz Bhatti Shahed Hospital Gujrat from Jan 2023 To Dec 2023 on 100 patients with knee OA (Kellgren-Lawrence grades I-III). The patients were divided into two groups: Group 1, consisting of 50 patients, received intra-articular HA injections, while Group 2, comprising the remaining 50 patients, received a combination of HA and PRP. Pain and functional outcomes were assessed using the Visual Analogue Scale (VAS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores at baseline, and at 2, 6, and 12-months post-injection. Results: Assessments at baseline, 2 months, 6 months, and 12 months post-injection revealed significant improvement in both groups compared to baseline. However, patients in Group 2 (HA+PRP) showed significantly greater improvement than those in Group 1 (HA alone), with WOMAC scores at 2-, 6-, and 12-months yielding p-values of 0.001*, 0.001*, and 0.000*, respectively. Conclusions: The study concluded that the combination of PRP and HA is more effective than HA alone in reducing pain and improving joint function in patients with knee OA. The findings suggest that PRP+HA offers superior benefits for managing knee osteoarthritis compared to HA alone.
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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 | 0.000 |
| 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".