Effect of Platelet-Rich Plasma (PRP) versus Autologous Whole Blood on Pain and Function Improvement in Tennis Elbow: A Randomized Clinical Trial
Bibliographic record
Abstract
Background. Autologous whole blood and platelet-rich plasma (PRP) have been both suggested to treat chronic tennis elbow. The aim of the present study was to compare the effects of PRP versus autologous whole blood local injection in chronic tennis elbow. Methods. Forty patients with tennis elbow were randomly divided into 2 groups. Group 1 was treated with a single injection of 2 mL of autologous PRP and group 2 with 2 mL of autologous blood. Tennis elbow strap, stretching, and strengthening exercises were administered for both groups during a 2-month followup. Pain and functional improvements were assessed using visual analog scale (VAS), modified Mayo Clinic performance index for the elbow, and pressure pain threshold (PPT) at 0, 4, and 8 weeks. Results. All pain and functional variables including VAS, PPT, and Mayo scores improved significantly in both groups 4 weeks after injection. No statistically significant difference was noted between groups regarding pain scores in 4-week follow-up examination (P > 0.05). At 8-week reevaluations, VAS and Mayo scores improved only in PRP group (P < 0.05). Conclusion. PRP and autologous whole blood injections are both effective to treat chronic lateral epicondylitis. PRP might be slightly superior in 8-week followup. However, further studies are suggested to get definite conclusion.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".