Quality of life changes in patients suffering from knee osteoarthritis treated with bone marrow aspirate concentrate, platelet-rich plasma and hyaluronic acid injections
Bibliographic record
Abstract
BACKGROUND: This study aimed to compare the effects of different treatments on quality of life in knee osteoarthritis patients. It focused on three therapies: bone marrow aspirate concentrate (BMAC), platelet-rich plasma (PRP), and hyaluronic acid (HA). METHODOLOGY: The trial was conducted at a single center with 175 patients over a 12-month period with the knee OA, KL grade II-IV. Outcomes were measured using the WOMAC (Western Ontario and McMaster Universities Osteoarthritis Index) and SF-36 scales, which assess physical and emotional well-being. Linear mixed models (LMMs) were used to analyze which treatment had the most positive impact on quality of life. RESULTS: ≤ 0,001). PRP outperformed HA in some aspects, but BMAC consistently led to greater gains. The most notable enhancements were seen in areas like role limitations due to physical health and overall physical functioning. CONCLUSIONS: The study suggested that BMAC treatment may contribute to improved quality of life in patients with knee osteoarthritis, particularly in terms of physical function. The correlation between WOMAC and SF-36 scores supports these findings, indicating a potential role for BMAC in enhancing mobility. CLINICAL TRIAL REGISTRATION: NCT03825133 (ClinicalTrials.gov).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".