Does the Source of Mesenchymal Stem Cell Have an Effect in the Management of Osteoarthritis of the Knee? Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Study Design Meta-analysis. Objectives To compare the efficacy and safety of bone marrow(BM)–derived mesenchymal stem cell(MSCs) and adipose-derived(AD) MSCs in the management of osteoarthritis of knee from randomized controlled trials(RCTs) available in the literature. Materials and Methods We conducted electronic database searche from PubMed, Embase, and Cochrane Library till May 2020 for RCTs analyzing the efficacy and safety of MSCs in management of osteoarthritis of knee. Visual Analog Score(VAS) for Pain, Western Ontario McMaster Universities Osteoarthritis Index(WOMAC), Lysholm Knee Scale(Lysholm), Whole-Organ Magnetic Resonance Imaging Score(WORMS), Knee Osteoarthritis Outcome Score(KOOS), and adverse events were the outcomes analyzed. Analysis was performed in R platform using OpenMeta[Analyst] software. Results Nineteen studies involving 811 patients were included for analysis. None of the studies compared the source of MSCs for osteoarthritis of knee and results were obtained by pooled data analysis of both sources. At 6 months, AD-MSCs showed significantly better VAS( P<0.001, P=0.069) and WOMAC( P=0.134, P=0.441) improvement than BM-MSCs, respectively, compared to controls. At 1 year, AD-MSCs outperformed BM-MSCs compared to their control in measures like WOMAC( P=0.007, P=0.150), KOOS( P<0.001; P=0.658), and WORMS( P<0.001, P=0.041), respectively. Similarly at 24 months, AD-MSCs showed significantly better Lysholm score( P=0.037) than BM-MSCs( P=0.807) although VAS improvement was better with BM-MSCs at 24 months( P<0.001). There were no significant adverse events with either of the MSCs compared to their controls. Conclusion Our analysis establishes the efficacy, safety, and superiority of AD-MSC transplantation, compared to BM-MSC, in the management of osteoarthritis of knee from available literature. Further RCTs are needed to evaluate them together with standardized doses.
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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 | 0.038 | 0.064 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.033 | 0.077 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".