AB142. 105. Platelet-rich plasma injections in hip osteoarthritis: a review
Bibliographic record
Abstract
Background: Osteoarthritis is a significant cause of chronic pain in the elderly population with hip osteoarthritis one of the main causes of functional disability and joint pain in adults older than 55 years. Intra-articular injections are commonly used to alleviate symptoms, with steroid and hyaluronic acid injections used most frequently Recently, platelet rich plasma (PRP) injections have been introduced for treatment of osteoarthritis. PRP has previously been shown to be effective in the treatment of tendinopathies and muscle tears. The goal of this study is to assess its effectiveness in the management of hip osteoarthritis. Methods: We performed a search of PubMed and Excerpta Medica dataBASE (EMBASE) for published randomised-controlled studies that assessed the effectiveness of PRP injections in the treatment of hip osteoarthritis, with a minimum follow up of six months. Primary outcome measures were Western Ontario and McMaster Universities Arthritis Index (WOMAC) and Visual Analogue Scale (VAS) scores. Results: Five trials were identified with 185 patients undergoing treatment with ultrasound-guided intra-articular injections of PRP, compared to patients treated with hyaluronic acid alone (n=148) or hyaluronic acid combined with PRP (n=31) in one study. PRP was shown to improve patient outcome scores at follow up at 6 and 12 months compared to baseline, however there was no significant difference seen between patients treated with PRP or hyaluronic acid alone. Conclusions: Platelet-rich plasma injections appear to offer a safe and effective treatment for hip osteoarthritis with improved patient outcomes up to 12 months following treatment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".