Analogies Between Platelet-Rich Plasma Versus Hyaluronic Acid Intra-articular Injections in the Treatment of Advanced Knee Arthritis: A Single-Center Study
Bibliographic record
Abstract
Background Knee osteoarthritis (KOA), a degenerative joint disease, is a common cause of chronic knee pain and disability in adults. Conservative management options are the first-line approach, but intra-articular injections, such as platelet-rich plasma (PRP) and hyaluronic acid (HA), are considered for advanced cases. This study aims to compare the efficacy of PRP versus HA injections in patients with advanced KOA. Methods A retrospective study was conducted on 145 patients with advanced KOA. Seventy patients received PRP injections, while 75 patients received HA injections. The Visual Analog Scale (VAS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score, and International Knee Documentation Committee (IKDC) score were employed to evaluate the treatment's efficacy. Adverse events associated with these injections were also recorded. Results Both PRP and HA injections significantly reduced pain and improved joint function in patients with advanced KOA. PRP injections were slightly more effective than HA injections in reducing pain scores. Both treatments showed similar improvements in functional outcomes. Adverse events were minimal and self-limiting for both treatments. Conclusions Both PRP and HA injections effectively ameliorate advanced KOA by reducing pain and improving function. PRP injections showed a slightly greater improvement in pain scores and functional outcomes. The choice between PRP and HA injections may depend on factors like cost, availability, and patient preference. Further research is needed to validate these findings and understand treatment suitability for different patient populations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".