Intra-Articular Injection of Autologous Micro-Fragmented Adipose Tissue for the Treatment of Knee Osteoarthritis: A Prospective Interventional Study
Bibliographic record
Abstract
Background: To investigate the efficacy and safety of autologous micro-fragmented adipose tissue (MF-AT) for improving joint function and cartilage repair in patients with knee osteoarthritis. Methods: From March 2019 to December 2020, 20 subjects (40 knees) between 50 and 65 years old suffering from knee osteoarthritis were enrolled in the study and administered a single injection of autologous MF-A. The data of all patients were prospectively collected. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), knee society score (KSS), hospital for special surgery (HSS) score, visual analogue score (VAS) pain score, changes in cartilage Recht grade on magnetic resonance imaging (MRI) and adverse events were analyzed before and 3, 6, 9, 12 and 18 months after injection. Results: The WOMAC, VAS, KSS and HSS scores at 3, 6, 9, 12 and 18 months after injection were improved compared with those before injection (p < 0.05). There was no significant difference in WOMAC scores between 9 and 12 months after injection (p > 0.05), but the WOMAC score 18 months after injection was worse than that at the last follow-up (p < 0.05). The VAS, KSS and HSS scores 9, 12 and 18 months after injection were worse than those at the last follow-up (p < 0.05). The Recht score improvement rate was 25%. No adverse events occurred during the follow-up. Conclusions: Autologous MF-AT improves knee function and relieves pain with no adverse events. However, the improved knee function was not sustained, with the best results occurring 9–12 months after injection and the cartilage regeneration remaining to be investigated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".