Platelet-rich plasma improves pain and function in knee osteoarthritis: a retrospective study
Bibliographic record
Abstract
Introduction: This study aimed to evaluate the efficacy of platelet-rich plasma (PRP) in treating knee osteoarthritis (KOA) and the effects of baseline characteristics and PRP intervention parameters on treatment outcomes. Methods: Overall, 140 individuals diagnosed with KOA who received PRP injections and completed a 6-month follow-up period were enrolled in this retrospective analysis. Knee pain and functional outcomes were assessed using the Visual Analog Scale (VAS) and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Based on the Minimal Clinically Important Difference (MCID) in outcomes, the participants were divided into effective and ineffective groups. Using multivariable logistic regression to explore factors influencing treatment outcomes, we compared the effective and ineffective groups to identify predictors of response to PRP therapy. Results: At 6 months, the median (IQR) VAS score significantly decreased from 66.5 (27) to 24 (34) (95% CI = -38 to -30.5), p < 0.001), and WOMAC scores improved from 29 (22) to 12 (14) (95% CI = -16.5 to -12), p < 0.001). Five mild adverse events were reported. Multivariate analysis indicated that only the number of injections significantly influenced VAS outcomes (OR = 4.285, 95% CI: 1.586-11.578, p = 0.004). Regarding WOMAC, multivariate analysis revealed that body mass index (BMI) (OR = 0.867, 95% CI: 0.755-0.995, p = 0.042) and disease duration (OR = 0.905, 95% CI: 0.784-0.989, p = 0.045) significantly affected outcomes. Age, sex, Kellgren-Lawrence (KL) grade, number of PRP injections, and injection frequency did not significantly impact WOMAC scores. Conclusion: PRP therapy is a safe and effective treatment option for KOA. In this 6-month follow-up investigation, we observed that the number of injections administered affected pain levels, while disease duration and BMI affected knee joint function. Insights from this study may facilitate patient selection and PRP treatment protocol optimization in clinical practice.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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".