Clinical evaluation of intra-articular platelet-rich plasma injection for the treatment of knee osteoarthritis
Bibliographic record
Abstract
Objective The aim of this study was to evaluate results of intra-articular injection of plasma sample rich in platelets for the treatment of knee osteoarthritis (OA). Background OA is a progressive, chronic condition leading to pain and loss of function that reduce patients' quality of life. Materials and methods The study was performed on 30 knees of 30 patients with age 30–65 years. All of the patients presented with knee OA grade I, II, and III and were treated with two intra-articular platelet-rich plasma (PRP) injections with three weeks interval. Preparation was done by centrifugation of 50 ml blood in two separate cycles, finally resulting in 5 ml of PRP then get activated by calcium chloride (CaCl2) at concentration of 50 μl/1 ml PRP, added just before injection. Western Ontario and McMaster universities score was used for clinical evaluation. Complications and adverse events were also recorded. Results Mean age of patients was 52 years. All of them were followed up to 12 months. The clinical results were classified on subjective base and were graded as excellent in 19 (63.3%) patients, good in six (20%) patients, and poor in five (16.7%) patients. No complications were noted. There was significant significance between BMI and clinical success (P = 0.000). The study showed significant association between grade of OA and clinical success (P = 0.008). Conclusion Maximal improvement occurred in a month and continued above basal levels in 12 months follow-up, especially in the young individuals. These findings indicate that treatment with PRP injections can reduce pain and improve knee function and quality of life with short-term efficacy.
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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 | 0.001 |
| 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".