The Role of PRP and Its Platelet Concentration in Improving WOMAC Score on Early-Stage Knee Osteoarthritis (OA) Patients
Bibliographic record
Abstract
Platelet concentration is one of the important factors in OA treatment with platelet-rich plasma (PRP). The purpose of this study was to determine the effect of PRP quality, which was determined by its platelet concentration, on Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores in early-stage knee OA patients. This study involved 50 patients diagnosed with early-stage knee OA (stage I to III). Twenty-five patients received PRP with moderate platelet concentration (1× to 4×), another twenty-five patients received PRP with high platelet concentration (>4× to 6×). Patients were evaluated using WOMAC questionnaire before and three months after injection with PRP. Bivariate analysis showed that there was a significant improvement of three WOMAC score subscales (pain, stiffness, and function) in both group (p<0.05) and a significant difference in the differences between WOMAC pain scores between the two groups (p<0.05), meanwhile, significant differences in the differences between WOMAC stiffness and function scores between the two groups weren’t found (p>0.05). The conclusions of this study was, PRP with moderate and high platelet concentration had been shown to improve the three WOMAC score subscales of early-stage knee OA patients, but the effect of PRP’s platelet concentration was only significant in the improvement of WOMAC pain score.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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".