<p>Platelet-Rich Plasma-Derived Growth Factor vs Hyaluronic Acid Injection in the Individuals with Knee Osteoarthritis: A One Year Randomized Clinical Trial</p>
Bibliographic record
Abstract
OBJECTIVE: In this study, we aimed at performing a comparison between intra-articular injections of PRP-derived growth factor (PGRF) and hyaluronic acid regarding their effect on pain and patient's function in knee osteoarthritis, as well as their safety profiles. METHODS: During our single-masked randomized clinical trial, the candidates with symptomatic knee osteoarthritis received two intra-articular injections of PRGF with 3 weeks apart or received three weekly injections of HA. The mean improvements from before treatment until the second, sixth, and twelfth months post-intervention in scores obtained by visual analog scale (VAS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and Lequesne index were our primary outcomes. RESULTS: A total of 102 candidates were finally included in the study. Patients' mean age was 57.08±7.3 years old in the PRGF group compared to the mean age of 58.63±7.09 years old in HA patients. In the PRGF group, total WOMAC index decreased from 41.96±11.71 to 27.10±12.3 (P = 0.02), and from 39.71±10.4 to 32.41±11.8 in the HA group after 12 months (P > 0.05). Regarding the Lequesne index, pain, ADL, and global scores significantly decreased after 12 months in the PRGF group compared to the HA group (P<0.001). There was also a meaningful higher rate of satisfaction in the PRGF group compared to the HA group after 12 months of treatment (P<0.001). CONCLUSION: Besides significantly higher satisfaction belonging to the PRGF group, there was a statistically significant improvement in VAS score and global, pain, and ADL score of Lequesne by passing 12 months from injection in PRGF compared to HA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".