Intra-articular platelet-rich plasma versus hyaluronic acid injections in patients with advanced knee osteoarthritis
Bibliographic record
Abstract
Background Osteoarthritis (OA) is a chronic disease that can be treated by several modality, one of which intra-articular injection. Hyaluronic acid (HA) and platelet-rich plasma (PRP) were approved in the management of OA grade 2 and grade 3 with good response. The aim of this study was to compare the effect of intra-articular injection of PRP versus HA in patients with knee OA grade 4. Patients and methods The study was carried out on 67 patients having knee OA grade 4, who were divided into two groups: group 1 included 33 patients who were treated with intra-articular injection of leukocyte-low PRP, and group 2 included 34 patients who were treated with intra-articular injection of high-molecular-weight hyaluronic acid. Both groups were evaluated according to the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and MRI before and 6 months after injection. Results In group 1, there were no statistically significant improvements in total WOMAC score and WOMAC score of pain, stiffness, and function in both knees and no statistically significant difference in cartilage thickness of the knee measured by MRI. In group 2, there were statistically significant improvement in total WOMAC score and WOMAC score of pain and function in both knees, no statistically significant improvement in WOMAC score of stiffness, and no statistically significant differences in cartilage thickness of the knee measured by MRI. Comparing the two groups, intra-articular injection of HA showed significant improvement than that of PRP in the management of grade 4 knees OA. Conclusions The effect of intra-articular injection of HA is better than that of PRP in the management of grade 4 knees OA.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".