Effects of Platelet-Rich Plasma on Pain and Muscle Strength in Patients With Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: No studies comparing the effects of platelet-rich plasma (PRP) injection and placebo injection in bilateral knee osteoarthritis in the same patient, or discussing muscle strength after PRP injection, have been published. DESIGN: Twenty patients with bilateral knee osteoarthritis were eligible, and 40 knees were randomized into two groups: PRP (knees [right or left by a coin toss] receiving a single intra-articular PRP injection) and saline group (the contralateral knee of the same patient, into which single 4-mL intra-articular injection of normal saline was administered). The primary outcome measure was Western Ontario and McMaster's Universities Osteoarthritis Index and the secondary included isokinetic test results. The evaluation was at baseline and at 2 wks, 1, 3, and 6 mos after injection. RESULTS: The PRP group showed a significant reduction in the Western Ontario and McMaster's Universities Osteoarthritis Index pain and total scores compared with normal saline group (P < 0.05). Although a significantly greater percentage of knee strength (extensor > flexor) was found in the PRP group during a longer follow-up period, PRP treatment resulted in insignificant differences in muscle strength compared with normal saline. CONCLUSIONS: Platelet-rich plasma treatment significantly improves pain, stiffness, and disability in patients with knee osteoarthritis compared with normal saline treatment. Additional strength training is recommended to enhance muscle strength recovery.
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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 | 0.000 |
| 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".