Results of Treating Mild to Moderate Knee Osteoarthritis with Autologous Conditioned Adipose Tissue and Leukocyte-Poor Platelet-Rich Plasma
Bibliographic record
Abstract
Knee osteoarthritis (KOA) is one of the most common musculoskeletal disorders. Much progress has been made in regenerative medicine for the symptomatic treatment of KOA, including products containing stromal vascular fraction (SVF) and platelet-rich plasma (PRP). The aim of this study was to evaluate clinical and radiological findings after the application of autologous conditioned adipose tissue (ACA) and leukocyte-poor PRP (LP-PRP) in patients with mild to moderate KOA. A total of 16 patients (eight male and eight female) with changes related to KOA on the magnetic resonance imaging (MRI), but without severe osteophytosis, full-thickness cartilage loss, or subchondral bone involvement were included in this study. Patients received an intraarticular, ultrasound-guided injection of ACA and LP-PRP. Clinical scores, including a visual analog scale for pain (VAS), Knee Injury and Osteoarthritis Outcome Score (KOOS), and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) were evaluated at baseline and at the three and six month follow-ups showing a statistically significant improvements at three and six months post-intervention. Furthermore, the delayed gadolinium-enhanced MRI of the cartilage (dGEMRIC) indices were evaluated at baseline and at the three and six month follow-ups showing no significant changes after treatment with ACA and LP-PRP, which were actually equal to the dGEMRIC indices measured in the control group (hyaluronic acid applied in contralateral knees without osteoarthritis). ACA with LP-PRP presents a viable minimally invasive therapeutic option for the clinical improvement of mild to moderate KOA. However, MFAT produced by different systems is likely to differ in cellular content, which can directly affect the paracrine effect (cytokine secretion) of mesenchymal stem cells and consequently the regeneration process.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".