Factors Associated with The Efficiency of Platelet-Rich Plasma Injection Among Patients with Knee Osteoarthritis: A Retrospective Data Analysis
Bibliographic record
Abstract
Abstract Objectives: Intra-articular application of platelet-rich plasma (PRP) are found to effectively improve knee function in patients with Knee osteoarthritis (KOA). This study is to identify the factors that influence the effect of PRP in the treatment of KOA. Methods: PRP was performed with a rich-leukocyte autologous conditioned plasma (ACP) system in 82 patients who were diagnosed with KOA. A Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) was used to score the level of KOA by 3 features as the evaluation indicator of therapeutic effect. Visual Analogue Scale (VAS), X-ray and subsequent Kellgren-Lawrence (K-L) scale level evaluation were performed before the treatment. Basic information of participants and disease duration were also collected. Simple correlation analysis, single-factor ANOVA and multivariant regression analysis were used for identifying factors associated with the efficiency of PRP injection. Results: WOMAC decreased after PRP therapy by 10.12 ± 0.98 points (P < 0.001). Simple correlation analysis and ANOVA suggest BMI, pre-treatment VAS scores, and pre-treatment K-L scale level positively correlated with the therapeutic effect of PRP treatment (P < 0.05), while the other factors were not significantly correlated with the effect (P > 0.05). Further multivariate model indicates that the responsible variable Y (Post-treatment changes of WOMAC scores) was affected by exploratory variables BMI (X1), VAS (X2), K-L scale level (X3) and Gender (X4) (F = 7.857, P < 0.001). The exploratory variable X3 had the biggest effect on Y.Conclusion: This study found that the therapeutic effect of PRP is better in male patients with higher BMI, higher VAS and lower K-L scale level.This study was qualified and registered in the Chinese Clinical Trial Registry as ChiCTR2000039856.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".