Platelet-rich plasma injection is more effective than hyaluronic acid in the treatment of knee osteoarthritis
Bibliographic record
Abstract
Objectives: To determine and compare the effects of autologous platelet rich plasma (PRP) and hyaluronic acid (HA) for the treatment of osteoarthritis of the knee. Methods: This prospective study included 150 patients affected by severe osteoarthritis of the knee. Gonarthrosis was graded using the Kellgren-Lawrence and Albhack radiographic classification scale. 150 patients were randomized into 2 study groups .In the PRP group (n=55) three intraarticular injection were applied andthe control group (n=55) received 3 intra-articular injections of high molecular weight HA. An unblinded physician performed infiltration once a week for 3 weeks into the knee affected by clinically relevant gonarthrosis (in both groups). All patients were evaluated with the Western Ontario and McMaster (WOMAC) score and visual pain scale before the infiltration and at 3, 6, and 12 months after the first injection. Results: No severe adverse events was observed. Statistically significant better results in the WOMAC score and visual pain scale was determined in PRP group than HA group at 3 months and 6 months follow up. The cost of the application for the PRP group was lower than that of the HA group.At 12 months' of follow-up, PRP and HA treatments offered similar results. Conclusion: The results of this study have shown the application of autologousPRP to be a safe, effective and low-cost method for treating OA tan HA. However, further studies are required for a more clear result
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".