Efficacy and safety of mesenchymal stem cells in knee osteoarthritis: a systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: The aim of this meta-analysis was to investigate the efficacy and safety of intra-articular injection of mesenchymal stem cells (MSCs) alone for the treatment of unoperated knee osteoarthritis (OA). METHODS: Four databases were systematically searched (before August 1, 2024) to include randomized controlled trials (RCTs) of MSCs for OA. The population of this study was OA patients who had not received any surgical treatment. The intervention was intra-articular injection of MSCs without other adjuvant therapy. Outcomes included Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), 100-mm Visual Analog Score (VAS), Knee Injury and Osteoarthritis Prognostic Score (KOOS), and adverse events. After screening the literature according to the eligibility criteria, extracting the data, and evaluating the quality, Meta-analysis was performed using Revman 5.3 software. The review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. RESULTS: 8 RCTs and 502 patients with OA were included in the study. Compared with the control group, MSCs significantly improved 6-month WOMAC [MD = 7.44, 95% CI = (1.45, 13.42), P = 0.01] and 12-month WOMAC [MD = 10.31, 95% CI = (0.96, 19.67), P = 0.03]. MSCs also improved VAS and KOOS at 6 and 12 months in patients with OA. Subgroup analysis showed more significant efficacy of adipose source and high doses of MSCs. There was no significant difference between the adverse events in the MSCs group and the control group (P > 0.05). CONCLUSION: Intra-articular injection of MSCs alone could significantly improve knee pain and dysfunction in patients with unoperated OA. MSCs are expected to be an effective treatment for OA with enhanced delivery efficiency.
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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 | 0.022 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.046 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".