Autologous Micro-fragmented Adipose Tissue in Patients With Knee Osteoarthritis: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Background: Microfragmented adipose tissue (MFAT)-containing mesenchymal stem cells (MSCs) combined with surgery is a promising strategy for the early management of knee osteoarthritis (KOA). This study aimed to explore the efficacy and safety of autologous MFAT-MSCs for the management of knee KOA.Methods: PubMed, Embase, the Cochrane Library, and Web of Science for potentially eligible studies published up to June 2021. The primary outcome was the Knee injury and Osteoarthritis Outcome Score (KOOS). The secondary outcomes were pain assessed by visual analog scale (VAS)/numeric rating scale (NRS), quality of life (QOL) (apart from the KOOS), and adverse events (AEs). The random-effects model was used in all analyses.Results: Eight studies (331 patients) were included. The mean differences in KOOS scores between pre-operation and post-operation (mean, 95%CI) were 22.1 (18.7, 25.3), 19.5 (15.4, 23.6), 23.0 (19.0, 26.9), 30.8 (25.5, 35.8), and 29.9 (24.8, 35.0) for pain, symptoms, ADL, sports/recreation, and QOL, respectively. The mean differences in pain VAS between pre-operation and post-operation were -3.026 (-3.884, -2.202). The mean differences in QOL between pre-operation and post-operation (mean, 95%CI) were -25.10 (-29.95, -20.20), 0.039 (-0.079, 0.170), and 0.33 (-0.99, 1.6) for the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), EQ-5D, and University of California in Los Angeles (UCLA), respectively. The use of MFAT-MSCs was not associated with bruising, bleeding, hematoma, drainage, infection, and swelling but was associated with soreness, pain, and stiffness.Conclusions: MFAT-MSC has potential benefits for KOA while being safe. A long-term follow-up and randomized controlled trials are necessary for confirmation.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.027 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".