Prognostic Factors Related to Clinical Response in 210 Knees Treated by Platelet-Rich Plasma for Osteoarthritis
Bibliographic record
Abstract
Many studies have shown the effectiveness of platelet-rich plasma (PRP) in the treatment of knee osteoarthritis. We aimed to determine the factors associated with good or poor response to PRP injections in knee osteoarthritis. This was a prospective observational study. Patients with knee osteoarthritis were recruited from a university hospital. PRP was injected twice at a one-month interval. Pain was assessed on a visual analog scale (VAS) and function was assessed using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Radiographic stage was collected and defined according to the Kellgren-Lawrence classification. Patients were classified as responders if they met the OMERACT-OARSI criteria at 7 months. We included 210 knees. At 7 months, 43.8% were classified as responders. Total WOMAC and VAS were significantly improved between M0 and M7. Physical therapy and a heel-buttock distance >35 cm were the two criteria associated with poor response at M7 by multivariate analysis. Pain VAS at M7 appeared to be lower in patients with osteoarthritis for less than 24 months. No adverse effects were reported. PRP treatment in knee osteoarthritis appears to be well-tolerated and effective, even in patients who reacted poorly to hyaluronic acid. Response was not associated with radiographic stage.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".