Single Intra-Articular Platelet-Rich Growth Factor Injection for Knee Osteoarthritis: Is It Effective in Severe Patients?
Bibliographic record
Abstract
Purpose: This study evaluated the clinical outcomes of intra-articular (IA) platelet-rich growth factor (PRGF) in patients with varying severities of knee osteoarthritis (KOA) using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score. It also examined whether IA PRGF could delay or prevent surgical intervention in patients with severe KOA.Methods: In this analytical observational cohort study, 120 patients with KOA, without systemic inflammatory disease or other intra-articular lesions, were classified using the Kellgren-Lawrence (KL) grading system. PRGF, a combination of leukocyte-rich platelet-rich plasma (LR-PRP) and injectable platelet-rich fibrin (iPRF), was prepared using the PP, GF, and ALPAS systems. A single 7 mL IA PRGF injection was administered. WOMAC scores were assessed at baseline, 1 week, and 1, 3, 6, and 12 months post-injection.Results: Ninety-six female and 21 male patients (mean age: 64.9±8.3 years) were included. Based on KL grading, 38 patients were classified as mild (grade I-II), 44 as moderate (grade III), and 35 as severe (grade IV). All groups showed a decline in WOMAC scores after PRGF injection. Although baseline scores were highest in the severe group, the pattern of score reduction was similar across all severities. WOMAC scores at 3 months were lower in the mild and moderate groups than in the severe group. At 12 months, all groups maintained significantly reduced scores compared to baseline.Conclusions: A single IA PRGF injection effectively improves pain, stiffness, and function in patients with severe KOA, with outcomes comparable to those in mild and moderate cases over 12 months of follow-up.
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 | 0.002 |
| 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) | 0.001 | 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".