Platelet Rich Plasma Injections for Knee Osteoarthritis Treatment: A Prospective Clinical Study
Bibliographic record
Abstract
Background: The aim of this prospective study was to evaluate the efficacy and safety of Platelet Rich Plasma (PRP) injections in patients affected by knee osteoarthritis (KOA). An autologous blood product containing a high percentage of various growth factors (GFs), cytokines and modulating factors as PRP has shown promising results in achieving this goal. Methods: One hundred and fifty-three patients (72 males, mean age 59.06 ± 8.78, range 40−81 years old) from January 2018 to January 2020 received three consecutive PRP injections and completed the follow ups. Western Ontario and McMaster University Osteoarthritis index (WOMAC), Knee society score (KSS) and Visual Analogic Scale (VAS) were evaluated before PRP injection (T0), one month (T1), three months (T2) and six months (T3) after the treatment. All patients underwent baseline and at 6 months MRI and X-ray evaluation. Results: A statistically significant VAS, KSS and WOMAC reduction emerged in the comparison between evaluations (p < 0.05), MRI demonstrated non-statistically significant improvement in cartilage thickness for both tibial plate and femoral plate (p = 0.46 and p = 0.33 respectively), and no radiographic changes could be seen in any patients. Conclusions: PRP injection represents a valid conservative treatment to reduce pain, improve quality of life and functional scores even at midterm of 6 months follow-up.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".