Comparative analysis of single-dose platelet-rich plasma and hyaluronic acid therapies in knee osteoarthritis: A 12-week follow-up study
Bibliographic record
Abstract
OBJECTIVE: Osteoarthritis (OA) is a prevalent and disabling joint condition that affects millions worldwide, particularly in the knee joint, and it presents limited therapeutic options. Platelet-rich plasma (PRP) and hyaluronic acid (HA) have emerged as promising intra-articular treatments. This study aimed to compare the effects of single-dose PRP and HA on pain, functionality, and stiffness in patients with knee OA over a 12-week follow-up period. METHODS: A retrospective analysis was conducted on 64 patients who underwent single-dose intra-articular HA or PRP treatment for knee OA between December 2021 and June 2022. Pain and functional outcomes were assessed using the Visual Analogue Scale (VAS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores. Patient satisfaction was evaluated using a Likert scale. Appropriate statistical analyses were performed to compare treatment outcomes and p<0.05 was considered statistically significant. RESULTS: Both PRP and HA treatments led to significant improvements in pain, functionality, and stiffness over the 12-week follow-up period. VAS pain scores decreased significantly in both groups, but a greater reduction was observed in the HA group. Additionally, the HA group exhibited superior improvement in the WOMAC physical function score at the 4-week mark (p=0.047). CONCLUSION: This study is another novel contribution to the growing literature on treatment of PRP and HA treatments for knee OA, where we highlighted the potential benefits of single-dose HA in alleviating pain and enhancing physical function.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".