111 Adipose Tissue-Derived Mesenchymal Stem Cells as a Potential Restorative Treatment for Cartilage Defects: A PRISMA Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Aim Joint damage through trauma or degeneration causes cartilage defects, leading to osteoarthritis (OA). Current therapies relieve symptoms or replaces damaged joint, which is costly and fraught with complications. Mesenchymal stem cells (MSCs) have immunomodulatory properties and low immunogenicity, making them a novel avenue for research for OA treatment. This systematic review investigates whether adipose derived MSC (AMSCs) can treat cartilage defects. Method A systematic search was performed on MEDLINE, EMBASE, Cochrane Library, Scopus, Web of Science. Clinical, imaging, functional outcomes were extracted from nineteen included studies. Inclusion criteria was studies conducted on human populations that compared effects of AMSCs on cartilage regeneration to non-exposed controls. Studies conducted on animals, ex vivo studies, in vitro studies were excluded. Results Nine studies reported improved Visual Analogue Scale (VAS) scores (mean difference -3.30; 95% CI:-3.72,-2.89; p<0.001). Eight studies reported improved Knee injury and Osteoarthritis Outcome Score (KOOS) in five subscales. Pooled analysis of Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores in seven studies revealed an improvement (mean difference -25.52; 95% CI:-30.93,-20.10; p<0.001). Cartilage regeneration was assessed using Magnetic Resonance Observation of Cartilage Repair Tissue (MOCART) score. All studies reported improved regeneration, with a pooled end-point score of 68.12 (95% CI:62.18–74.05; p<0.001). Conclusions AMSCs are effective therapeutic agents for cartilage defects. We recommend researchers to determine roles of biochemical components that facilitate AMSC-mediated cartilage repair. Establishing the most efficient methods for MSC extraction, culture, delivery, and performing studies with long follow-up times enable future research to provide evidence needed to bring AMSC-based therapies into the market.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".