To Analyze the Effect of Platelet Count of Platelet-rich Plasma on Clinical Outcomes in Patients of Osteoarthritis of Knee
Bibliographic record
Abstract
Abstract Purpose: The purpose of this study was to find the association between the platelet (PLT) count in PLT-rich plasma (PRP) injection and clinical response in knee osteoarthritis (KOA) patients. Materials and Methods: Fifty patients of Kellgren–Lawrence Grade 2 and 3 KOA were given single intra-articular PRP injection. Baseline PLT count was documented and PLT concentration of each PRP was documented and was classified as P1, P2, and P3 (as per PAW classification). This was compared to clinical results using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and Visual Analog Scale (VAS) score at baseline, followed by 3, 6, and 9 months of follow-up. Results: There was a significant decrease in VAS score between the P1 and P2 and P1 and P3 groups at 9-month follow-up, with P = 0.028 (<0.05) and P = 0.01 (<0.05), respectively. Moreover, the WOMAC score significantly dropped among the P1 and P2 and P1 and P3 groups at 9-month follow-up, with P = 0.016 (<0.05) and P = 0.036 (<0.05), respectively. Conclusion: Both the VAS score and the WOMAC score have significantly decreased at 9-month follow-up period when the P1 group is compared to both P2 and P3 but not between P2 and P3, so it is concluded that for proper efficacy of PRP injection in OA knee Grades 2 and 3, concentration of platelets in prepared PRP should be more than baseline PLT concentration.
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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 | 0.001 |
| 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) | 0.001 | 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".