Mid-term outcomes of microfragmented adipose tissue plus arthroscopic surgery for knee osteoarthritis: A randomized, active-control, multicenter clinical trial
Bibliographic record
Abstract
BACKGROUND Osteoarthritis (OA) is the most prevalent form of degenerative whole-joint disease. Before the final option of knee replacement, arthroscopic surgery was the most widely used joint-preserving surgical treatment. Emerging regenerative therapies, such as those involving platelet-rich plasma, mesenchymal stem cells, and microfragmented adipose tissue (MFAT), have been pushed to the forefront of treatment to prevent the progression of OA. Currently, MFAT has been successfully applied to treat different types of orthopedic diseases. AIM To assess the efficacy and safety of MFAT with arthroscopic surgery in patients with knee OA (KOA). METHODS A randomized, multicenter study was conducted between June 2017 and November 2022 in 10 hospitals in Zhejiang, China. Overall, 302 patients diagnosed with KOA (Kellgren-Lawrence grades 2-3) were randomized to the MFAT group (n = 151, were administered MFAT following arthroscopic surgery), or the control group (n = 151, were administered hyaluronic acid following arthroscopic surgery). The study outcomes were changes in the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score, the visual analog scale (VAS) score, the Lequesne index score, the Whole-Organ Magnetic Resonance Imaging Score (WORMS), and safety over a 24-mo period from baseline. RESULTS The changes in the WOMAC score (including the three subscale scores), VAS pain score, and Lequesne index score at the 24-mo mark were significantly different in the MFAT and control groups, as well as when comparing values at the posttreatment visit and those at baseline (P < 0.001). The MFAT group consistently demonstrated significant decreases in the WOMAC pain scores and VAS scores at all follow-ups compared to the control group (P < 0.05). Furthermore, the WOMAC stiffness score, WOMAC function score, and Lequesne index score differed significantly between the groups at 12 and 24 mo (P < 0.05). However, no significant between-group differences were observed in the WORMS at 24 mo (P = 0.367). No serious adverse events occurred in both groups. CONCLUSION The MFAT injection combined with arthroscopic surgery treatment group showed better mid-term clinical outcomes compared to the control group, suggesting its efficacy as a therapeutic approach for patients with KOA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".